Topic: dp

Improved Surgical Visibility and Navigation during Endoscopic Treatment of Upper Tract Urothelial Carcinoma

Project Summary The importance of localizing and treating all upper tract urothelial cancer (UTUC) tumors during a renal sparing, endoscopic treatment is emphasized by the high risk of cancer progression from inadequate tumor treatment. Insufficient treatment necessitates kidney and ureteral removal (i.e., nephroureterectomy). Nephroureterectomy permanently compromises renal function, and increases morbidity and mortality, while negatively impacting a patient’s quality of life. In contrast, endoscopic treatment (i.e., using a laser to ablate only the tumors) improves long-term outcomes by sparing healthy kidney tissue. However, endoscopic treatment is underutilized compared to nephroureterectomy because it is difficult to accomplish. Successful endoscopic treatment is dependent on the surgeon’s ability to create a mental 3D map of the branched, intrarenal endoscopic anatomy intraoperatively from preoperative 2D imaging, which is extremely difficult. Since mental mapping relies on hand-eye coordination, memory, and spatial reasoning, it is inherently imprecise and its impact on accuracy and tumor treatment is dependent on the surgeon’s experience. To make matters worse, even when tumors are successfully visualized, the surgeon often cannot accurately assess the location of tumor margins or infer pathologic grade due to the limited field of view and depth of field (10mm and 6mm on average, respectively) of current scopes. The scopes only provide visualization of a small part of the surgical field at any instant. These inherent challenges prevent many surgeons from attempting endoscopic tumor treatment since incomplete treatment leads to a devastating, oncologic outcome. Our overall goal is to create an enhanced visualization and navigational system that makes endoscopic UTUC tumor treatment easier and more accurate for all surgeons, enabling wider utilization. Toward this goal, our specific objective in this proposal is to test the hypothesis that our system can make endoscopic UTUC surgery more accurate and efficient. To test this hypothesis, we propose three Specific Aims: Aim 1 involves the development of an automatic, real-time segmentation and grading system of UTUC tumors during endoscopic treatment. Aim 2 integrates a 3D navigational map of collecting system anatomy, which includes tumor and endoscope location, during endoscopic surgery. Aim 3 evaluates the system in patients, with zero risk to the human subjects. The endpoint of this R01 will be a fully validated enhanced visualization and navigational system for endoscopic UTUC surgery, which would provide the necessary experimental data towards a large-scale, multi-center clinical trial and future FDA approval. As our system would require only software integration to current endoscopic surgical cameras, all existing endoscopic surgical systems could in principle immediately benefit from the results of this project. In this way, we believe the success of our project will facilitate improved UTUC treatment and mitigate progression to a higher risk extirpative surgery.

Targeting disulfidptosis in cancer: mechanisms and preclinical translation

Project Summary Studying regulated cell death is critical for our understanding of cellular homeostasis and tumor suppression. We recently discovered disulfidptosis as a new form of regulated cell death induced by disulfide stress under NADPH-depleting conditions in SLC7A11-high cancer cells. However, in contrast to our deep understanding of other cell death modalities such as apoptosis and ferroptosis, the molecular and metabolic underpinnings of disulfidptosis, along with its therapeutic implications, remain largely unexplored. The objectives of this application are to elucidate the mechanisms underlying disulfidptosis and to therapeutically target this form of cell death in SLC7A11-high cancers. The proposed studies will make extensive use of human cancer cell lines and integrated human cellbased molecular analyses, including metabolomics, proteomics, CRISPR screening, and biochemical studies, to define the metabolic and signaling mechanisms governing disulfidptosis. In addition, select in vivo studies are incorporated in the therapeutic validation components of the project, where tumor growth response, systemic drug exposure and tolerability, tumor microenvironmental influences, and host immune/stromal interactions must be evaluated in an organismal context to ensure translational rigor. Alternative in vitro systems such as organoids may provide useful complementary information on tumor-intrinsic responses, but they cannot fully recapitulate the systemic metabolic stress, pharmacologic exposure, and organism-level therapeutic efficacy required for these studies. It is expected that our proposed studies will reveal novel mechanisms underlying disulfidptosis and identify effective therapies to induce this form of cell death in SLC7A11-high cancers. Our proposal is highly innovative because it focuses on a previously unexplored cell death pathway in cancer therapy. Our proposed studies will have significant impact on both our understanding of the fundamental mechanisms of disulfidptosis and our ability to target this cell death pathway in cancer treatment.

Metabolic Assessment of Metformin in Pregnancy (MoM-P)

PROJECT SUMMARY The objective of the “Metabolic Assessment of Metformin in Pregnancy “(MoM-P) proposal is to assess the physiological effect of metformin on maternal and neonatal metabolism during pregnancy in individuals developing gestational diabetes (GDM). Metformin is increasingly being used for medical treatment of GDM not adequately treated with nutrition and physical activity. There is inconsistency among various organizations (Society for Maternal Fetal Medicine, American College of Obstetrics and Gynecology and the American Diabetes Association) as to metformin’s role in the medical management of GDM. We will examine the metabolic action of metformin in GDM pregnancies and effect on mothers and their offspring. We plan to recruit 50 participants from Massachusetts General Hospital (MGH) for Specific Aims 1, 2 and 3 and 100 participants from Ohio State University college of Medicine (OSUCOM) for Specific Aims 2 and 3. Participants for the study will have been diagnosed with GDM requiring medical management of GDM as part of the DECIDE multicenter randomized controlled trial. The primary site for DECIDE is OSUCOM, with Dr. Mark Landon as the PI. The MoM-P study will recruit participants from the DECIDE trial at MGH and OSUCOM. The MoM-P study aims are: Aim 1: To establish metformin’s effects on endogenous (primarily hepatic) glucose production (EGP) and insulin sensitivity in late pregnancy. We hypothesize that metformin does not lower EGP in pregnancy and hence the need of additional insulin in the medical management of GDM. We will perform infusion of a stable isotope of glucose (6,6 2H2 glucose) to estimate EGP and a HOMA-IR prior to initiation of medical management and again at 37 weeks gestation. Aim 2: Metformin increases GDF15 levels in human GDM pregnancy and is associated with lower nutrient intake, gestational weight gain (GWG) and increased resting energy expenditure (REE). We hypothesize that metformin increases GDF15 concentrations which lead to GI upset, lower caloric intake/GWG and increases REE. In DECIDE participants randomized to metformin vs. insulin, we will measure GDF15 and examine the relationship to ASA-nutrition records, REE with indirect calorimetry and maternal body composition using air displacement plethysmography (ADP) prior to initiation of medication and again at 37 weeks. Aim 3: To compare fetal growth and body composition in neonates exposed and unexposed to metformin in utero. We hypothesize that metformin treatment of GDM decreases fetal weight: 1) directly based on metformin’s effect on neonatal metabolism (fetal AMPK and mTOR pathways) and 2) indirectly by lowering maternal nutritional intake, fat free mass (FFM) and increasing maternal REE, resulting in decreased neonatal FFM and increased fat mass in childhood. In DECIDE participants, we will measure neonatal body composition with 72 hours of delivery using pediatric ADP and a planned follow-up of children at 2 years in the DECIDE protocol with estimates of male and female children’s body composition.

Linking Single-Cell Transcriptomic, Morphological, and Temporal Signatures of Vulnerability in Neurodegeneration

Neurodegeneration involves complex cellular phenotypes and molecular changes that vary widely among the cells of the nervous system. Current methodologies permit either detailed molecular profiling (e.g., single-cell transcriptomics) or functional phenotyping (e.g., live imaging of neuronal activity), but not both in the same cells. Thus, it is difficult to directly link a neuron's functional state or fate with its gene expression profile. To address this limitation, we developed an innovative technology, VISTA-FISH (Video Imaging with Spatial- Temporal Analysis by FISH), that couples prospective live-cell imaging with high-resolution spatial transcriptomic profiling of the same cells. This approach enables in situ comparisons of gene expression in neurons that exhibit divergent behaviors or outcomes. Using VISTA-FISH, we will profile iPS-derived human neurons to link single-cell gene expression, morphology, and temporal phenotypes to study molecular pathways driving resilience as well as susceptibility. After exposing neurons carrying TDP43 and C9orf72 mutations to a stimulus inducing TDP43 aggregation, we will jointly record TDP43 localization and neuron activity using live-cell microscopy, then measure single-cell gene expression of the same cells (Aim 1). We will also combine live-cell measurements of TDP43 half-life with CRISPR screening and single-cell gene expression (Aim 2). These rich datasets will enable us to determine transcriptomic changes associated with differences in protein aggregation, protein synthesis, and protein degradation in individual cells, providing an unprecedented molecular perspective on factors responsible for vulnerability and resilience to neurodegeneration.

Impact of environmental toxicants on frontal cortical circuits

Abstract: Human mercury (Hg) exposure has been known for many decades to produce cognitive impairment and mood disorder symptoms. Hg is a global pollutant that poses widespread potential for neurotoxic exposure, earning it a position on the WHO’s list of the top 10 chemicals of major public health concern. However, little is known about the neural mechanisms that lead to neuropsychiatric symptoms from Hg exposure. The objective of this application is to identify specific mechanisms, within the neocortical circuits that control emotion and cognition, that are disrupted by the neurotoxicant, methylmercury (MeHg). The neocortex exhibits especially strong bioaccumulation of Hg, magnifying the risk to these circuits. Therefore, we hypothesize that chronic MeHg exposure leads to persistent circuit dysfunction in prefrontal and insular cortices (mPFC and aIC) – two brain regions critical in control of emotion and cognition. Our recent work showed that mPFC neurons in brain slices are negatively affected by acute MeHg exposure, resulting in hyperexcitability and altered synaptic transmission. Currently, it unknown how these acute effects on synaptic transmission translate to altered neuronal function in vivo. This proposal applies an integrative approach to determine the in vivo effects of MeHg on mPFC and aIC circuits, at the systems neurophysiology, synaptic and molecular levels. We will compare the effects of MeHg exposure on in vivo spiking activity patterns in brain regions of the mPFC-aIC circuit, using multiunit electrophysiological recordings in awake animals. Action potentials will be recorded simultaneously from multiple neurons, distributed across cortical layers, to evaluate effects on spike frequency, temporal patterning and correlation. Using acute brain slices derived from animals chronically treated with MeHg in vivo, electrophysiologically recorded synaptic estimates will be made to compare the effects of MeHg exposure on synaptic transmission and EI-balance within brain regions of the mPFC-aIC circuit. Based on previous evidence, we hypothesize that TDP-43 hyper-phosphorylation and aggregation link MeHg exposure to mPFC and aIC dysfunction. Therefore, immunohistochemistry will be used to measure TDP-43 hyper-phosphorylation and nuclear redistribution from animals treated in vivo +/- MeHg. In addition, tissue will be co-labeled with antibodies for nPAS4, a well-stablished molecular marker of activity, to determine whether TDP-43 hallmarks correlate with MeHg-induced hyper-excitability. The results of our study will substantively improve our mechanistic understanding of how Hg disrupts frontal cortical function and contribute to our understanding of the biological basis of emotional and cognitive sympoms. Identifying specific actions of MeHg at the functional microcircuitry level and cellular/molecular level will help significantly in finding novel targets for therapeutic interventions. If our hypothesis is correct, this will also raise the question of the extent to which chronic low-level environmental mercury exposure contributes to the etiology of fronto-cortical disorders with symptoms that overlap mercury exposure but do not have definitive genetic origins. This is particularly important because fronto-cortical disorders are predominantly sporadic in nature.

Understanding antiretroviral phosphorylation and dephosphorylation using mass spectrometry imaging-based enzyme histochemistry

PROJECT SUMMARY Our overall goal is to understand the mechanistic differences in the activation and deactivation of two widely used first-line antiretroviral drugs: tenofovir (TFV) and emtricitabine (FTC) in colonic tissues. HIV is a global health problem and roughly 1.3 million people became newly infected with HIV globally in 2022. Pre-exposure prophylaxis (PrEP) is an HIV prevention strategy where HIV-negative individuals use antiretrovirals to reduce the risk of HIV infection. Specifically, oral fixed-dose combinations of two antiretrovirals, namely, TFV (TFV; prescribed as TFV disoproxil fumarate or TFV alafenamide prodrugs) and FTC are FDA-approved for HIV PrEP. The pharmacologically active forms of TFV and FTC are TFV-diphosphate (TFV-DP) and FTC-triphosphate (FTC-TP), respectively, and these phosphorylated metabolites are found in cells. Unfortunately, high variability in the responses of TFV and FTC can lead to poor clinical outcomes, including therapeutic failure. However, the molecular mechanisms responsible for the observed variability in TFV and FTC responses are poorly understood. Although the observed variability in TFV and FTC drug responses is likely to be multifactorial, alterations in drug activation and deactivation can contribute to the observed variability in drug responses. Phosphorylation of TFV is known and recent studies suggest that nucleotidases may involve in the dephosphorylation of TFV metabolites. Although the kinases that phosphorylate FTC in peripheral blood mononuclear cells are known, the kinases that are responsible for the phosphorylation of FTC in putative sites of HIV infection such as colonic tissues are yet to be determined. Notably, unprotected receptive anal intercourse has a 20-fold higher risk of HIV transmission than vaginal intercourse. Thus, understanding the biotransformation of TFV and FTC in colonic tissue is important since it is a susceptible tissue to HIV infection. Recently, we have reported the enzymatic activities of nucleotidases toward the pharmacologically active metabolites of TFV and FTC in vitro. However, the mechanistic details of the biotransformation of the above drugs in HIV susceptible tissues such as colonic tissues are yet to be elucidated. Gaining a mechanistic understanding of the biotransformation of TFV and FTC in putative sites of HIV infection is important to improve their therapeutic efficacy. As such, in this application, we propose an innovative mass spectrometry imaging-based interdisciplinary approach to understand the biotransformation of TFV and FTC in the colon. Aim 1 will establish the role of nucleotide kinases and nucleotidases in regulating TFV and FTC metabolites in colonic cells mechanistically. Aim 2 will characterize the region- and cell-type-specific expression patterns, as well as enzymatic activities of nucleotide kinases and nucleotidases in situ. The proposed project will provide novel understandings of TFV and FTC activating and deactivating mechanisms that can be leveraged to optimize the therapeutic efficacy of the above drugs.

Expanding mechanisms and therapeutic targets for neurodegenerative disease

A hallmark pathological feature of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is the depletion of RNA-binding protein TDP-43 from the nucleus of neurons in the brain and spinal cord. A major function of TDP-43 is as a repressor of cryptic exon inclusion during RNA splicing. By re-analyzing RNA-sequencing datasets from human FTD/ALS brains, we discovered dozens of novel cryptic splicing events in important neuronal genes. Single nucleotide polymorphisms in UNC13A are among the strongest hits associated with FTD and ALS in human genome-wide association studies, but how those variants increase risk for disease is unknown. We discovered that TDP-43 represses a cryptic exon-splicing event in UNC13A. Loss of TDP-43 from the nucleus in human brain, neuronal cell lines and motor neurons derived from induced pluripotent stem cells resulted in the inclusion of a cryptic exon in UNC13A mRNA and reduced UNC13A protein expression. The top variants associated with FTD or ALS risk in humans are located in the intron harboring the cryptic exon, and we show that they increase UNC13A cryptic exon splicing in the face of TDP-43 dysfunction. Together, our data provide a direct functional link between one of the strongest genetic risk factors for FTD and ALS (UNC13A genetic variants), and loss of TDP-43 function. Recent analyses have revealed even further changes in TDP-43 target genes, including widespread changes in alternative polyadenylation, impacting expression of disease-relevant genes (e.g., ELP1, NEFL, and TMEM106B) and providing evidence that alternative polyadenylation is a new facet of TDP-43 pathology.

Learning and Memory

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphasizing how the mushroom body—a key site for associative learning—implements a high-dimensional, random representation of sensory features. Convergent dopaminergic inputs gate plasticity, reflecting a high-dimensional “critic” that refines behavior. Feedback loops within the mushroom body further reveal sophisticated interactions between learning signals and action selection. Gjorgieva examines how activity-dependent plasticity rules shape circuitry from the subcellular (e.g., synaptic clustering on dendrites) to the cortical network level. She demonstrates how spontaneous activity during development, Hebbian competition, and inhibitory-excitatory balance collectively establish connectivity motifs responsible for key computations such as response normalization.

Feedback-induced dispositional changes in risk preferences

Contrary to the original normative decision-making standpoint, empirical studies have repeatedly reported that risk preferences are affected by the disclosure of choice outcomes (feedback). Although no consensus has yet emerged regarding the properties and mechanisms of this effect, a widespread and intuitive hypothesis is that repeated feedback affects risk preferences by means of a learning effect, which alters the representation of subjective probabilities. Here, we ran a series of seven experiments (N= 538), tailored to decipher the effects of feedback on risk preferences. Our results indicate that the presence of feedback consistently increases risk-taking, even when the risky option is economically less advantageous. Crucially, risk-taking increases just after the instructions, before participants experience any feedback. These results challenge the learning account, and advocate for a dispositional effect, induced by the mere anticipation of feedback information. Epistemic curiosity and regret avoidance may drive this effect in partial and complete feedback conditions, respectively.

Deepfake Detection in Super-Recognizers and Police Officers

Using videos from the Deepfake Detection Challenge (cf. Groh et al., 2021), we investigated human deepfake detection performance (DDP) in two unique observer groups: Super-Recognizers (SRs) and "normal" officers from within the 18K members of the Berlin Police. SRs were identified either via previously proposed lab-based procedures (Ramon, 2021) or the only existing tool for SR identification involving increasingly challenging, authentic forensic material: beSure® (Berlin Test For Super-Recognizer Identification; Ramon & Rjosk, 2022). Across two experiments we examined deepfake detection performance (DDP) in participants who judged single videos and pairs of videos in a 2AFC decision setting. We explored speed-accuracy trade-offs in DDP, compared DDP between lab-identified SRs and non-SRs, and police officers whose face identity processing skills had been extensively tested using challenging. In this talk I will discuss our surprising findings and argue that further work is needed too determine whether face identity processing is related to DDP or not.

Prefrontal mechanisms involved in learning distractor-resistant working memory in a dual task

Working memory (WM) is a cognitive function that allows the short-term maintenance and manipulation of information when no longer accessible to the senses. It relies on temporarily storing stimulus features in the activity of neuronal populations. To preserve these dynamics from distraction it has been proposed that pre and post-distraction population activity decomposes into orthogonal subspaces. If orthogonalization is necessary to avoid WM distraction, it should emerge as performance in the task improves. We sought evidence of WM orthogonalization learning and the underlying mechanisms by analyzing calcium imaging data from the prelimbic (PrL) and anterior cingulate (ACC) cortices of mice as they learned to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of protecting the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. To gain mechanistic insight into how these low-dimensional WM representations evolve with learning we built a recurrent spiking network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which induced early-training neural dynamics in well-trained animals. In the model, learning drives the network from a double-well attractor toward a more continuous ring attractor regime. We tested signatures for this dynamical evolution in the experimental data by estimating the energy landscape of the dynamics on a one-dimensional ring. In sum, our study defines network dynamics underlying the process of learning to shield WM representations from distracting tasks.

Obesity and Brain – Bidirectional Influences

The regulation of body weight relies on homeostatic mechanisms that use a combination of internal signals and external cues to initiate and terminate food intake. Homeostasis depends on intricate communication between the body and the hypothalamus involving numerous neural and hormonal signals. However, there is growing evidence that higher-level cognitive function may also influence energy balance. For instance, research has shown that BMI is consistently linked to various brain, cognitive, and personality measures, implicating executive, reward, and attentional systems. Moreover, the rise in obesity rates over the past half-century is attributed to the affordability and widespread availability of highly processed foods, a phenomenon that contradicts the idea that food intake is solely regulated by homeostasis. I will suggest that prefrontal systems involved in value computation and motivation act to limit food overconsumption when food is scarce or expensive, but promote over-eating when food is abundant, an optimum strategy from an economic standpoint. I will review the genetic and neuroscience literature on the CNS control of body weight. I will present recent studies supporting a role of prefrontal systems in weight control. I will also present contradictory evidence showing that frontal executive and cognitive findings in obesity may be a consequence not a cause of increased hunger. Finally I will review the effects of obesity on brain anatomy and function. Chronic adiposity leads to cerebrovascular dysfunction, cortical thinning, and cognitive impairment. As the most common preventable risk factor for dementia, obesity poses a significant threat to brain health. I will conclude by reviewing evidence for treatment of obesity in adults to prevent brain disease.

Uncovering the molecular effectors of diet and exercise

Despite the profound effects of nutrition and physical activity on human health, our understanding of the molecules mediating the salutary effects of specific foods or activities remains remarkably limited. Here, we share our ongoing studies that use unbiased and high-resolution metabolomics technologies to uncover the molecules and molecular effectors of diet and exercise. We describe how exercise stimulates the production of Lac-Phe, a blood-borne signaling metabolite that suppresses feeding and obesity. Ablation of Lac-Phe biosynthesis in mice increases food intake and obesity after exercise. We also describe the discovery of an orphan metabolite, BHB-Phe. Ketosis-inducible BHB-Phe is a congener of exercise-inducible Lac-Phe, produced in CNDP2+ cells when levels of BHB are high, and functions to lower body weight and adiposity in ketosis. Our data uncover an unexpected and underappreciated signaling role for metabolic fuel derivatives in mediating the cardiometabolic benefits of diet and exercise. These data also suggest that diet and exercise may mediate their physiologic effects on energy balance via a common family of molecules and overlapping signaling pathways.

Programmed axon death: from animal models into human disease

Programmed axon death is a widespread and completely preventable mechanism in injury and disease. Mouse and Drosophila studies define a molecular pathway involving activation of SARM1 NA Dase and its prevention by NAD synthesising enzyme NMNAT2 . Loss of axonal NMNAT2 causes its substrate, NMN , to accumulate and activate SARM1 , driving loss of NAD and changes in ATP , ROS and calcium. Animal models caused by genetic mutation, toxins, viruses or metabolic defects can be alleviated by blocking programmed axon death, for example models of CMT1B , chemotherapy-induced peripheral neuropathy (CIPN), rabies and diabetic peripheral neuropathy (DPN). The perinatal lethality of NMNAT2 null mice is completely rescued, restoring a normal, healthy lifespan. Animal models lack the genetic and environmental diversity present in human populations and this is problematic for modelling gene-environment combinations, for example in CIPN and DPN , and identifying rare, pathogenic mutations. Instead, by testing human gene variants in WGS datasets for loss- and gain-of-function, we identified enrichment of rare SARM1 gain-of-function variants in sporadic ALS , despite previous negative findings in SOD1 transgenic mice. We have shown in mice that heterozygous SARM1 loss-of-function is protective from a range of axonal stresses and that naturally-occurring SARM1 loss-of-function alleles are present in human populations. This enables new approaches to identify disorders where blocking SARM1 may be therapeutically useful, and the existence of two dominant negative human variants in healthy adults is some of the best evidence available that drugs blocking SARM1 are likely to be safe. Further loss- and gain-of-function variants in SARM1 and NMNAT2 are being identified and used to extend and strengthen the evidence of association with neurological disorders. We aim to identify diseases, and specific patients, in whom SARM1 -blocking drugs are most likely to be effective.

Versatile treadmill system for measuring locomotion and neural activity in head-fixed mice

Here, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. https://dx.doi.org/10.1016/j.xpro.2022.101701

PET imaging in brain diseases

Talk 1. PET based biomarkers of treatment efficacy in temporal lobe epilepsy A critical aspect of drug development involves identifying robust biomarkers of treatment response for use as surrogate endpoints in clinical trials. However, these biomarkers also have the capacity to inform mechanisms of disease pathogenesis and therapeutic efficacy. In this webinar, Dr Bianca Jupp will report on a series of studies using the GABAA PET ligand, [18F]-Flumazenil, to establish biomarkers of treatment response to a novel therapeutic for temporal lobe epilepsy, identifying affinity at this receptor as a key predictor of treatment outcome. Dr Bianca Jupp is a Research Fellow in the Department of Neuroscience, Monash University and Lead PET/CT Scientist at the Alfred Research Alliance–Monash Biomedical Imaging facility. Her research focuses on neuroimaging and its capacity to inform the neurobiology underlying neurological and neuropsychiatric disorders. Talk 2. The development of a PET radiotracer for reparative microglia Imaging of neuroinflammation is currently hindered by the technical limitations associated with TSPO imaging. In this webinar, Dr Lucy Vivash will discuss the development of PET radiotracers that specifically image reparative microglia through targeting the receptor kinase MerTK. This includes medicinal chemistry design and testing, radiochemistry, and in vitro and in vivo testing of lead tracers. Dr Lucy Vivash is a Research Fellow in the Department of Neuroscience, Monash University. Her research focuses on the preclinical development and clinical translation of novel PET radiotracers for the imaging of neurodegenerative diseases.

How do protein-RNA condensates form and contribute to disease?

In recent years, it has become clear that intrinsically disordered regions (IDRs) of RBPs, and the structure of RNAs, often contribute to the condensation of RNPs. To understand the transcriptomic features of such RNP condensates, we’ve used an improved individual nucleotide resolution CLIP protocol (iiCLIP), which produces highly sensitive and specific data, and thus enables quantitative comparisons of interactions across conditions (Lee et al., 2021). This showed how the IDR-dependent condensation properties of TDP-43 specify its RNA binding and regulatory repertoire (Hallegger et al., 2021). Moreover, we developed software for discovery and visualisation of RNA binding motifs that uncovered common binding patterns of RBPs on long multivalent RNA regions that are composed of dispersed motif clusters (Kuret et al, 2021). Finally, we used hybrid iCLIP (hiCLIP) to characterise the RNA structures mediating the assembly of Staufen RNPs across mammalian brain development, which demonstrated the roles of long-range RNA duplexes in the compaction of long 3’UTRs. I will present how the combined analysis of the characteristics of IDRs in RBPs, multivalent RNA regions and RNA structures is required to understand the formation and functions of RNP condensates, and how they change in diseases.

Optimization at the Single Neuron Level:​ Prediction of Spike Sequences and Emergence of Synaptic Plasticity Mechanisms

Intelligent behavior depends on the brain’s ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on pre-dictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory motion signaling and recall in the visual system. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.

Metabolic spikes: from rogue electrons to Parkinson's

Conventionally, neurons are thought to be cellular units that process synaptic inputs into synaptic spikes. However, it is well known that neurons can also spike spontaneously and display a rich repertoire of firing properties with no apparent functional relevance e.g. in in vitro cortical slice preparations. In this talk, I will propose a hypothesis according to which intrinsic excitability in neurons may be a survival mechanism to minimize toxic byproducts of the cell’s energy metabolism. In neurons, this toxicity can arise when mitochondrial ATP production stalls due to limited ADP. Under these conditions, electrons deviate from the electron transport chain to produce reactive oxygen species, disrupting many cellular processes and challenging cell survival. To mitigate this, neurons may engage in ADP-producing metabolic spikes. I will explore the validity of this hypothesis using computational models that illustrate the implications of synaptic and metabolic spiking, especially in the context of substantia nigra pars compacta dopaminergic neurons and their degeneration in Parkinson's disease.

A nonlinear shot noise model for calcium-based synaptic plasticity

Activity dependent synaptic plasticity is considered to be a primary mechanism underlying learning and memory. Yet it is unclear whether plasticity rules such as STDP measured in vitro apply in vivo. Network models with STDP predict that activity patterns (e.g., place-cell spatial selectivity) should change much faster than observed experimentally. We address this gap by investigating a nonlinear calcium-based plasticity rule fit to experiments done in physiological conditions. In this model, LTP and LTD result from intracellular calcium transients arising almost exclusively from synchronous coactivation of pre- and postsynaptic neurons. We analytically approximate the full distribution of nonlinear calcium transients as a function of pre- and postsynaptic firing rates, and temporal correlations. This analysis directly relates activity statistics that can be measured in vivo to the changes in synaptic efficacy they cause. Our results highlight that both high-firing rates and temporal correlations can lead to significant changes to synaptic efficacy. Using a mean-field theory, we show that the nonlinear plasticity rule, without any fine-tuning, gives a stable, unimodal synaptic weight distribution characterized by many strong synapses which remain stable over long periods of time, consistent with electrophysiological and behavioral studies. Moreover, our theory explains how memories encoded by strong synapses can be preferentially stabilized by the plasticity rule. We confirmed our analytical results in a spiking recurrent network. Interestingly, although most synapses are weak and undergo rapid turnover, the fraction of strong synapses are sufficient for supporting realistic spiking dynamics and serve to maintain the network’s cluster structure. Our results provide a mechanistic understanding of how stable memories may emerge on the behavioral level from an STDP rule measured in physiological conditions. Furthermore, the plasticity rule we investigate is mathematically equivalent to other learning rules which rely on the statistics of coincidences, so we expect that our formalism will be useful to study other learning processes beyond the calcium-based plasticity rule.

NMC4 Short Talk: Systematic exploration of neuron type differences in standard plasticity protocols employing a novel pathway based plasticity rule

Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for comparison of plasticity in different neuron types. The plastic updates depend on the relative strength and activation of separately modeled LTP and LTD pathways, which are modulated by glutamate release and postsynaptic voltage. We used the 15 neuron type parametrizations in the GLIF5 model presented by Teeter et al. (2018) in combination with the VDP to simulate a range of standard plasticity protocols including standard STDP experiments, frequency dependency experiments and low frequency stimulation protocols. Slight variation in kernel stability and frequency effects can be identified between the neuron types, suggesting that the neuron type may have an effect on the effective learning rule. This plasticity model builds a middle ground between biophysical and phenomenological models allowing not just for the combination with more complex and biophysical neuron models, but is also computationally efficient so can be used in network simulations. Therefore it offers the possibility to explore the functional role of the different kernel types and electrophysiological differences in heterogeneous networks in future work.

NMC4 Short Talk: What can deep reinforcement learning tell us about human motor learning and vice-versa ?

In the deep reinforcement learning (RL) community, motor control problems are usually approached from a reward-based learning perspective. However, humans are often believed to learn motor control through directed error-based learning. Within this learning setting, the control system is assumed to have access to exact error signals and their gradients with respect to the control signal. This is unlike reward-based learning, in which errors are assumed to be unsigned, encoding relative successes and failures. Here, we try to understand the relation between these two approaches, reward- and error- based learning, and ballistic arm reaches. To do so, we test canonical (deep) RL algorithms on a well-known sensorimotor perturbation in neuroscience: mirror-reversal of visual feedback during arm reaching. This test leads us to propose a potentially novel RL algorithm, denoted as model-based deterministic policy gradient (MB-DPG). This RL algorithm draws inspiration from error-based learning to qualitatively reproduce human reaching performance under mirror-reversal. Next, we show MB-DPG outperforms the other canonical (deep) RL algorithms on a single- and a multi- target ballistic reaching task, based on a biomechanical model of the human arm. Finally, we propose MB-DPG may provide an efficient computational framework to help explain error-based learning in neuroscience.

Parp mutations protect from mitochondrial toxicity in Alzheimer’s disease

Alzheimer’s disease is the most common age-related neurodegenerative disorder. Familial forms of Alzheimer’s disease associated with the accumulation of a toxic form of amyloid-β (Aβ) peptides are linked to mitochondrial impairment. The coenzyme nicotinamide adenine dinucleotide (NAD+) is essential for both mitochondrial bioenergetics and nuclear DNA repair through NAD+-consuming poly (ADP-ribose) polymerases (PARPs). Here, we analysed the metabolomic changes in flies over-expressing Aβ and showed a decrease of metabolites associated with nicotinate and nicotinamide metabolism, which is critical for mitochondrial function in neurons. We show that increasing the bioavailability of NAD+ protects against Aβ toxicity. Pharmacological supplementation using NAM, a form of vitamin B that acts as a precursor for NAD+ or a genetic mutation of PARP rescues mitochondrial defects, protects neurons against degeneration and reduces behavioural impairments in a fly model of Alzheimer’s disease. Next, we looked at links between PARP polymorphisms and vitamin B intake in patients with Alzheimer’s disease. We show that polymorphisms in the human PARP1 gene or the intake of vitamin B, are associated with a decrease in the risk and severity of Alzheimer’s disease. We suggest that enhancing the availability of NAD+ by either vitamin B supplements or the inhibition of NAD+-dependent enzymes, such as PARPs are potential therapies for Alzheimer’s disease.

Co-tuned, balanced excitation and inhibition in olfactory memory networks

Odor memories are exceptionally robust and essential for the survival of many species. In rodents, the olfactory cortex shows features of an autoassociative memory network and plays a key role in the retrieval of olfactory memories (Meissner-Bernard et al., 2019). Interestingly, the telencephalic area Dp, the zebrafish homolog of olfactory cortex, transiently enters a state of precise balance during the presentation of an odor (Rupprecht and Friedrich, 2018). This state is characterized by large synaptic conductances (relative to the resting conductance) and by co-tuning of excitation and inhibition in odor space and in time at the level of individual neurons. Our aim is to understand how this precise synaptic balance affects memory function. For this purpose, we build a simplified, yet biologically plausible spiking neural network model of Dp using experimental observations as constraints: besides precise balance, key features of Dp dynamics include low firing rates, odor-specific population activity and a dominance of recurrent inputs from Dp neurons relative to afferent inputs from neurons in the olfactory bulb. To achieve co-tuning of excitation and inhibition, we introduce structured connectivity by increasing connection probabilities and/or strength among ensembles of excitatory and inhibitory neurons. These ensembles are therefore structural memories of activity patterns representing specific odors. They form functional inhibitory-stabilized subnetworks, as identified by the “paradoxical effect” signature (Tsodyks et al., 1997): inhibition of inhibitory “memory” neurons leads to an increase of their activity. We investigate the benefits of co-tuning for olfactory and memory processing, by comparing inhibitory-stabilized networks with and without co-tuning. We find that co-tuned excitation and inhibition improves robustness to noise, pattern completion and pattern separation. In other words, retrieval of stored information from partial or degraded sensory inputs is enhanced, which is relevant in light of the instability of the olfactory environment. Furthermore, in co-tuned networks, odor-evoked activation of stored patterns does not persist after removal of the stimulus and may therefore subserve fast pattern classification. These findings provide valuable insights into the computations performed by the olfactory cortex, and into general effects of balanced state dynamics in associative memory networks.

Error correction and reliability timescale in converging cortical networks

Rapidly changing inputs such as visual scenes and auditory landscapes are transmitted over several synaptic interfaces and perceived with little loss of detail, but individual neurons are typically “noisy” and cortico-cortical connections are typically “weak”. To understand how information embodied in spike train is transmitted in a lossless manner, we focus on a single synaptic interface: between pyramidal cells and putative interneurons. Using arbitrary white noise patterns injected intra-cortically as photocurrents to freely-moving mice, we find that directly-activated cells exhibit precision of several milliseconds, but post-synaptic, indirectly-activated cells exhibit higher precision. Considering multiple identical messages, the reliability of directly-activated cells peaks at a timescale of dozens of milliseconds, whereas indirectly-activated cells exhibit an order-of-magnitude faster timescale. Using data-driven modelling, we find that error correction is consistent with non-linear amplification of coincident spikes.

STDP and the transfer of rhythmic signals in the brain

Rhythmic activity in the brain has been reported in relation to a wide range of cognitive processes. Changes in the rhythmic activity have been related to pathological states. These observations raise the question of the origin of these rhythms: can the mechanisms responsible for generation of these rhythms and that allow the propagation of the rhythmic signal be acquired via a process of learning? In my talk I will focus on spike timing dependent plasticity (STDP) and examine under what conditions this unsupervised learning rule can facilitate the propagation of rhythmic activity downstream in the central nervous system. Next, the I will apply the theory of STDP to the whisker system and demonstrate how STDP can shape the distribution of preferred phases of firing in a downstream population. Interestingly, in both these cases STDP dynamics does not relax to a fixed-point solution, rather the synaptic weights remain dynamic. Nevertheless, STDP allows for the system to retain its functionality in the face of continuous remodeling of the entire synaptic population.

Distinct synaptic plasticity mechanisms determine the diversity of cortical responses during behavior

Spike trains recorded from the cortex of behaving animals can be complex, highly variable from trial to trial, and therefore challenging to interpret. A fraction of cells exhibit trial-averaged responses with obvious task-related features such as pure tone frequency tuning in auditory cortex. However, a substantial number of cells (including cells in primary sensory cortex) do not appear to fire in a task-related manner and are often neglected from analysis. We recently used a novel single-trial, spike-timing-based analysis to show that both classically responsive and non-classically responsive cortical neurons contain significant information about sensory stimuli and behavioral decisions suggesting that non-classically responsive cells may play an underappreciated role in perception and behavior. We now expand this investigation to explore the synaptic origins and potential contribution of these cells to network function. To do so, we trained a novel spiking recurrent neural network model that incorporates spike-timing-dependent plasticity (STDP) mechanisms to perform the same task as behaving animals. By leveraging excitatory and inhibitory plasticity rules this model reproduces neurons with response profiles that are consistent with previously published experimental data, including classically responsive and non-classically responsive neurons. We found that both classically responsive and non-classically responsive neurons encode behavioral variables in their spike times as seen in vivo. Interestingly, plasticity in excitatory-to-excitatory synapses increased the proportion of non-classically responsive neurons and may play a significant role in determining response profiles. Finally, our model also makes predictions about the synaptic origins of classically and non-classically responsive neurons which we can compare to in vivo whole-cell recordings taken from the auditory cortex of behaving animals. This approach successfully recapitulates heterogeneous response profiles measured from behaving animals and provides a powerful lens for exploring large-scale neuronal dynamics and the plasticity rules that shape them.

Programmed Axon Death and its Roles in Human Disease

Axons degenerate before the neuronal soma in many neurodegenerative diseases. Programmed axon death (Wallerian degeneration) is a widely-occurring mechanism of axon loss that is well understood and preventable in animals. Its aberrant activation by mutation of the pro-survival gene Nmnat2 directly causes axonopathy in mice with severity ranging from mild polyneuropathy to perinatal lethality. Rare biallelic mutations in the homologous human gene cause related phenotypes in patients. NMNAT2 is a negative regulator of the prodegenerative NADase SARM1. Constitutive activation of SARM1 is cytotoxic and the human SARM1 locus is significantly associated with sporadic ALS. Another negative regulator, STMN2, has also been implicated in ALS, where it is commonly depleted downstream of TDP-43. In mice, programmed axon death can be robustly blocked by deletion of Sarm1, or by overexpression, axonal targeting and/or stabilization of various NMNAT isoforms. This alleviates models of many human disorders including some forms of peripheral neuropathy, motor neuron diseases, glaucoma, Parkinson’s disease and traumatic brain injury, and it confers lifelong rescue on the lethal Nmnat2 null phenotype and other conditions. Drug discovery programs now aim to achieve similar outcomes in human disease. In order to optimize the use of such drugs, we have characterized a range of human NMNAT2 and SARM1 functional variants that underlie a spectrum of axon vulnerability in the human population. Individuals at the vulnerable end of this spectrum are those most likely to benefit from drugs blocking programmed axon death, and disorders associated with these genotypes are promising indications in which to apply them.

Carnosine negatively modulates pro-oxidant activities of M1 peripheral macrophages and prevents neuroinflammation induced by amyloid-β in microglial cells

Carnosine is a natural dipeptide widely distributed in mammalian tissues and exists at particularly high concentrations in skeletal and cardiac muscles and brain. A growing body of evidence shows that carnosine is involved in many cellular defense mechanisms against oxidative stress, including inhibition of amyloid-β (Aβ) aggregation, modulation of nitric oxide (NO) metabolism, and scavenging both reactive nitrogen and oxygen species. Different types of cells are involved in the innate immune response, with macrophage cells representing those primarily activated, especially under different diseases characterized by oxidative stress and systemic inflammation such as depression and cardiovascular disorders. Microglia, the tissue-resident macrophages of the brain, are emerging as a central player in regulating key pathways in central nervous system inflammation; with specific regard to Alzheimer’s disease (AD) these cells exert a dual role: on one hand promoting the clearance of Aβ via phagocytosis, on the other hand increasing neuroinflammation through the secretion of inflammatory mediators and free radicals. The activity of carnosine was tested in an in vitro model of macrophage activation (M1) (RAW 264.7 cells stimulated with LPS + IFN-γ) and in a well-validated model of Aβ-induced neuroinflammation (BV-2 microglia treated with Aβ oligomers). An ample set of techniques/assays including MTT assay, trypan blue exclusion test, high performance liquid chromatography, high-throughput real-time PCR, western blot, atomic force microscopy, microchip electrophoresis coupled to laser-induced fluorescence, and ELISA aimed to evaluate the antioxidant and anti-inflammatory activities of carnosine was employed. In our experimental model of macrophage activation (M1), therapeutic concentrations of carnosine exerted the following effects: 1) an increased degradation rate of NO into its non-toxic end-products nitrite and nitrate; 2) the amelioration of the macrophage energy state, by restoring nucleoside triphosphates and counterbalancing the changes in ATP/ADP, NAD+/NADH and NADP+/NADPH ratio obtained by LPS + IFN-γ induction; 3) a reduced expression of pro-oxidant enzymes (NADPH oxidase, Cyclooxygenase-2) and of the lipid peroxidation product malondialdehyde; 4) the rescue of antioxidant enzymes expression (Glutathione peroxidase 1, Superoxide dismutase 2, Catalase); 5) an increased synthesis of transforming growth factor-β1 (TGF-β1) combined with the negative modulation of interleukines 1β and 6 (IL-1β and IL-6), and 6) the induction of nuclear factor erythroid-derived 2-like 2 (Nrf2) and heme oxygenase-1 (HO-1). In our experimental model of Aβ-induced neuroinflammation, carnosine: 1) prevented cell death in BV-2 cells challenged with Aβ oligomers; 2) lowered oxidative stress by decreasing the expression of inducible nitric oxide synthase and NADPH oxidase, and the concentrations of nitric oxide and superoxide anion; 3) decreased the secretion of pro-inflammatory cytokines such as IL-1β simultaneously rescuing IL-10 levels and increasing the expression and the release of TGF-β1; 4) prevented Aβ-induced neurodegeneration in primary mixed neuronal cultures challenged with Aβ oligomers and these neuroprotective effects was completely abolished by SB431542, a selective inhibitor of type-1 TGF-β receptor. Overall, our data suggest a novel multimodal mechanism of action of carnosine underlying its protective effects in macrophages and microglia and the therapeutic potential of this dipeptide in counteracting pro-oxidant and pro-inflammatory phenomena observed in different disorders characterized by elevated levels of oxidative stress and inflammation such as depression, cardiovascular disorders, and Alzheimer’s disease.

On the purpose and origin of spontaneous neural activity

Spontaneous firing, observed in many neurons, is often attributed to ion channel or network level noise. Cortical cells during slow wave sleep exhibit transitions between so called Up and Down states. In this sleep state, with limited sensory stimuli, neurons fire in the Up state. Spontaneous firing is also observed in slices of cholinergic interneurons, cerebellar Purkinje cells and even brainstem inspiratory neurons. In such in vitro preparations, where the functional relevance is long lost, neurons continue to display a rich repertoire of firing properties. It is perplexing that these neurons, instead of saving their energy during information downtime and functional irrelevance, are eager to fire. We propose that spontaneous firing is not a chance event but instead, a vital activity for the well-being of a neuron. We postulate that neurons, in anticipation of synaptic inputs, keep their ATP levels at maximum. As recovery from inputs requires most of the energy resources, neurons are ATP surplus and ADP scarce during synaptic quiescence. With ADP as the rate-limiting step, ATP production stalls in the mitochondria when ADP is low. This leads to toxic Reactive Oxygen Species (ROS) formation, which are known to disrupt many cellular processes. We hypothesize that spontaneous firing occurs at these conditions - as a release valve to spend energy and to restore ATP production, shielding the neuron against ROS. By linking a mitochondrial metabolism model to a conductance-based neuron model, we show that spontaneous firing depends on baseline ATP usage and on ATP-cost-per-spike. From our model, emerges a mitochondrial mediated homeostatic mechanism that provides a recipe for different firing patterns. Our findings, though mostly affecting intracellular dynamics, may have large knock-on effects on the nature of neural coding. Hitherto it has been thought that the neural code is optimised for energy minimisation, but this may be true only when neurons do not experience synaptic quiescence.

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons

Irina is a research scientist at DeepMind, where she works in the Froniers team. Her work aims to bring together insights from the fields of neuroscience and physics to advance general artificial intelligence through improved representation learning. Before joining DeepMind, Irina was a British Psychological Society Undergraduate Award winner for her achievements as an undergraduate student in Experimental Psychology at Westminster University, followed by a DPhil at the Oxford Centre for Computational Neuroscience and Artificial Intelligence, where she focused on understanding the computational principles underlying speech processing in the auditory brain. During her DPhil, Irina also worked on developing poker AI, applying machine learning in the finance sector, and working on speech recognition at Google Research."" https://arxiv.org/pdf/2006.14304.pdf

Analogical Reasoning and Executive Functions - A Life Span Approach

From a developmental standpoint, it has been argued that two major complementary factors contribute to the development of analogy comprehension: world knowledge and executive functions. Here I will provide evidence in support of the second view. Beyond paradigms that manipulate task difficulty (e.g., number and types of distractors and semantic distance between domains) we will provide eye-tracking data that describes differences in the way children and adults compare the base and target domains in analogy problems. We will follow the same approach with ageing people. This latter population provides a unique opportunity to disentangle the contribution of knowledge and executive processes in analogy making since knowledge is (more than) preserved and executive control is decreasing. Using this paradigm, I will show the extent to which world knowledge (assessed through vocabulary) compensates for decreasing executive control in older populations. Our eye-tracking data suggests that, to a certain extent, differences between younger and older adults are analogous to the differences between younger adults and children in the way they compare the base and the target domains in analogy problems.

Future data services sandpit: transforming discovery and access

Analysis of the synaptic contribution of the DPYSL5 gene involved in neurodevelopmental disorders

C9ORF72 and GRN mutations sensitize microglia to pro-inflammatory activation by extracellular TDP-43

Characterization of a therapeutic approach to target intracellular TDP-43 aggregates in cellular and animal models of Amyotrophic Lateral Sclerosis

Cognitive impairment in Dp(10)2Yey mouse model of Down syndrome is associated with altered neural dynamics and changes in medial prefrontal cortex and hippocampal cellular biology

A combined TDP43-Tau cellular model for the understanding of LATE-NC Proteinopathy

Dendritic processing implements spike-timing dependent plasticity (STDP) in cerebellar Golgi cells

Determination of the number of CNVs in the SNCA gene by ddPCR

Dynamics of TDP43 axonal transport and its interaction with the kinesin-1 motor machinery

Investigating the role of microglial TDP-43 in brain development

Investigation of the functional interaction between TDP-43 and SMN in the genetic zebrafish model of SMA and in patient derived motor neurons

Modular representation of reaching endpoints in mouse motor cortex

Motor And Cognitive Behavioral Alterations In A Novel Rat Model Of TDP-43 Overexpression

Peripheral inflammatory changes around motor axon terminals in the TDP-43 mouse model of amyotrophic lateral sclerosis

Preclinical evaluation of CK-1δ and TTBK1 inhibitors in a TDP-43-related frontotemporal dementia mouse model

Role of Metabolism in Pathological Aggregation of TDP-43 and its Down-Stream Toxicity