Neurodegeneration, characterized by the progressive loss of neuronal structure or function, is commonly assessed in clinical practice through reductions in cortical thickness or brain volume, as visualized by structural MRI. While informative, these conventional approaches lack the statistical sophistication required to fully capture the spatially correlated and heterogeneous nature of neurodegeneration, which manifests both in healthy aging and in neurological disorders. To address these limitations, brain age gap has emerged as a promising data-driven biomarker of brain health. The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from neuroimaging data and their chronological age. The resulting brain age gap serves as a compact biomarker of brain health, with recent studies demonstrating its predictive utility for disease progression and severity. However, practical adoption of BAGP models is hindered by their methodological obscurities and limited generalizability across diverse clinical populations. This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP). In particular, we focus on graph neural networks (GNNs) and introduce the coVariance neural network (VNN), which leverages the anatomical covariance matrices derived from structural MRI. VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions. By integrating perspectives from GSP, machine learning, and network neuroscience, this work clarifies the path forward for reliable and interpretable BAGP models and outlines future research directions in personalized medicine.

Modelling the underlying mechanisms of neurodegenerative diseases demands methods that capture heterogeneous and spatially varying dynamics from sparse, high-dimensional neuroimaging data. Integrating partial differential equation (PDE) based physics knowledge with machine learning provides enhanced interpretability and utility over classic numerical methods. However, current physics-integrated machine learning methods are limited to considering a single PDE, severely limiting their application to diseases where multiple mechanisms are responsible for different groups (i.e., subtypes) and aggravating problems with model misspecification and degeneracy. Here, we present a deep generative model for learning mixtures of latent dynamic models governed by physics-based PDEs, going beyond traditional approaches that assume a single PDE structure. Our method integrates reaction-diffusion PDEs within a variational autoencoder (VAE) mixture model framework, supporting inference of subtypes of interpretable latent variables (e.g. diffusivity and reaction rates) from neuroimaging data. We evaluate our method on synthetic benchmarks and demonstrate its potential for uncovering mechanistic subtypes of Alzheimer's disease progression from positron emission tomography (PET) data.

Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized disease trajectories remain limited. Here, we present a machine learning-based operator learning framework for personalized modeling of AD progression, integrating longitudinal multimodal imaging, biomarker, and clinical data. Unlike conventional models with prespecified dynamics, our approach directly learns patient-specific disease operators governing the spatiotemporal evolution of amyloid, tau, and neurodegeneration biomarkers. Using Laplacian eigenfunction bases, we construct geometry-aware neural operators capable of capturing complex brain dynamics. Embedded within a digital twin paradigm, the framework enables individualized predictions, simulation of therapeutic interventions, and in silico clinical trials. Applied to AD clinical data, our method achieves high prediction accuracy exceeding 90% across multiple biomarkers, substantially outperforming existing approaches. This work offers a scalable, interpretable platform for precision modeling and personalized therapeutic optimization in neurodegenerative diseases.

Creating controlled methods to simulate neurodegeneration in artificial intelligence (AI) is crucial for applications that emulate brain function decline and cognitive disorders. We use IQ tests performed by Large Language Models (LLMs) and, more specifically, the LLaMA 2 to introduce the concept of ``neural erosion." This deliberate erosion involves ablating synapses or neurons, or adding Gaussian noise during or after training, resulting in a controlled progressive decline in the LLMs' performance. We are able to describe the neurodegeneration in the IQ tests and show that the LLM first loses its mathematical abilities and then its linguistic abilities, while further losing its ability to understand the questions. To the best of our knowledge, this is the first work that models neurodegeneration with text data, compared to other works that operate in the computer vision domain. Finally, we draw similarities between our study and cognitive decline clinical studies involving test subjects. We find that with the application of neurodegenerative methods, LLMs lose abstract thinking abilities, followed by mathematical degradation, and ultimately, a loss in linguistic ability, responding to prompts incoherently. These findings are in accordance with human studies.

Alzheimer's disease (AD), the predominant form of dementia, poses a growing global challenge and underscores the urgency of accurate and early diagnosis. The clinical technique radiologists adopt for distinguishing between mild cognitive impairment (MCI) and AD using Machine Resonance Imaging (MRI) encounter hurdles because they are not consistent and reliable. Machine learning has been shown to offer promise for early AD diagnosis. However, existing models focused on focal fine-grain features without considerations to focal structural features that give off information on neurodegeneration of the brain cerebral cortex. Therefore, this paper proposes a machine learning (ML) framework that integrates Gamma correction, an image enhancement technique, and includes a structure-focused neurodegeneration convolutional neural network (CNN) architecture called SNeurodCNN for discriminating between AD and MCI. The ML framework leverages the mid-sagittal and para-sagittal brain image viewpoints of the structure-focused Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through experiments, our proposed machine learning framework shows exceptional performance. The parasagittal viewpoint set achieves 97.8% accuracy, with 97.0% specificity and 98.5% sensitivity. The midsagittal viewpoint is shown to present deeper insights into the structural brain changes given the increase in accuracy, specificity, and sensitivity, which are 98.1% 97.2%, and 99.0%, respectively. Using GradCAM technique, we show that our proposed model is capable of capturing the structural dynamics of MCI and AD which exist about the frontal lobe, occipital lobe, cerebellum, and parietal lobe. Therefore, our model itself as a potential brain structural change Digi-Biomarker for early diagnosis of AD.

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

With artificial intelligence steadily infiltrating nearly every aspect of modern life, there is a healthy amount of skepticism about the outsize role of machine learning in life in the twenty-first century. For many, concerns over the implications of technologies such as facial recognition, online tracking of individual preferences, and robots assuming roles formerly filled by humans raise understandable alarm. Fortunately, the medical sector provides a powerful example of artificial intelligence being leveraged to support rather than compete with human intelligence. Generally speaking, our brain health remains somewhat of a black box until symptoms of cognitive decline present and our brain activity is finally monitored through advanced medical testing. While most of us visit our primary care physician at least once a year for a comprehensive review of our physical health, no such proactive mechanism currently exists with regard to our cognitive health.

Alzheimer's disease (AD) is characterized by brain protein aggregates of amyloid-β (Aβ) and phosphorylated tau (pTau) that become plaques and tangles, and dystrophic neurites surrounding the plaques, which are accompanied by downstream neurodegeneration. These protein changes can be used as biomarkers detected through positron emission tomography (PET) imaging and in cerebrospinal fluid (CSF), allowing for ATN (amyloid, tau, and neurodegeneration) classification of patients. This phenotyping has become standard in AD clinical trials to overcome the high misclassification rate (20 to 30%) for clinical criteria and also enables enrollment of preclinical AD patients. The recent approval of the first disease-modifying anti-amyloid immunotherapy, aducanumab, for AD will generate a need for widely accessible and inexpensive biomarkers for ATN classification of patients with cognitive complaints. Technological advances have also overcome the challenges of measuring the extraordinarily low amounts of brain-derived proteins in blood samples, and recent studies indicate that AD blood tests may soon be possible. The Aβ42 variant of Aβ is aggregation-prone and is deposited in plaques in the brains of people with AD, whereas the shorter Aβ40 isoform is by far the most abundant Aβ peptide (see the figure). Thus, as AD progresses and Aβ42 forms plaques, its concentration in the CSF and blood is reduced. Ascertaining the ratio of Aβ42 and Aβ40 concentrations in the CSF is known to adjust for between-individual differences in “total” Aβ production, thereby increasing concordance with amyloid PET imaging to detect brain amyloidosis. Applying the same principle for blood plasma Aβ, immunoprecipitation–mass spectrometry (IP-MS) measures of plasma Aβ42/Aβ40 ratio can reach an accuracy exceeding 90% to identify brain amyloidosis ([ 1 ][1]). A population-based study of 441 asymptomatic elderly individuals indicates that IP-MS plasma Aβ can identify those who are amyloid PET-positive with high accuracy ([ 2 ][2]). ![Figure][3] Biomarkers of Alzheimer's disease Low amyloid-β (Aβ) 42/40 isoform ratio is associated with brain amyloidosis, and several phosphorylated tau (pTau) fragments increase with tau pathology; both are specific blood biomarkers for Alzheimer's disease (AD). Among neurodegeneration biomarkers, neurofilament light (NFL) is modestly increased in AD, and total tau (T-tau) is markedly increased only in cerebrospinal fluid (CSF), and not blood, in AD. Glial fibrillary acidic protein (GFAP) is a candidate blood biomarker for astrocytic activation, to indicate neuroinflammation. GRAPHIC: KELLIE HOLOSKI/ SCIENCE The question then arises whether plasma Aβ detection can replace PET or CSF tests for brain amyloidosis. A potential issue is that Aβ is produced not only in the brain but also in platelets and peripheral tissues, which will obscure the central nervous system–derived Aβ signal in plasma. Consequently, in amyloid PET-positive cases, plasma Aβ42/Aβ40 ratio is only ∼10% lower than in individuals without brain amyloidosis, whereas it is more than 40% lower in CSF ([ 3 ][4]). This leads to an overlap that introduces challenges to robustly classify individuals as being either amyloid positive or negative, especially in those with Aβ42/Aβ40 ratios close to the cut-off for normality. Algorithms combining plasma Aβ42/Aβ40 ratio with the ϵ4 variant of apolipoprotein E ( APOE ), which is the major AD risk gene, and age (the main risk factor for AD) increase accuracy in detecting brain amyloidosis by 2 to 6% ([ 2 ][2], [ 3 ][4]). However, merging biomarker data with genetic risk and aging may cause confusion because some younger APOE -ϵ4 noncarriers with low plasma Aβ42/Aβ40 will be misclassified as amyloid negative by the algorithm, whereas a proportion of older individuals with homozygous APOE -ϵ4 but normal plasma Aβ42/Aβ40 will be wrongly classified as amyloid positive. Tau protein is truncated into amino-terminal to mid-domain fragments before being secreted in blood plasma and CSF ([ 4 ][5]). CSF pTau has long been used as an AD-specific biomarker. A major breakthrough is the use of new ultrasensitive methods that allow for quantification of pTau in blood plasma, with high concentrations occurring in AD ([ 5 ][6]). Of 321 patients and controls, high plasma concentrations of pTau181 fragments were associated with brain tau pathology as measured by PET ([ 6 ][7]). Similar results were subsequently presented for other pTau species, including pTau217 ([ 7 ][8]) and pTau231 ([ 8 ][9]). The findings of very high accuracy of plasma pTau217 in the ability to discriminate AD from other neurodegenerative disorders ([ 7 ][8]) and IP-MS data showing a higher magnitude of increase and better association with amyloid plaques by PET of plasma pTau217 than of pTau181 ([ 4 ][5]) suggest that there may be diagnostic or pathophysiological differences between pTau species, but this remains a matter of debate. Nonetheless, these pTau blood biomarkers all show high concordance with AD pathology at autopsy, with accuracies in differentiating AD from non-AD dementia cases up to 99% for pTau231 ([ 8 ][9]). However, these studies are based on different analytical methods and cohorts. In an attempt to directly compare these pTau species, a study of 381 participants employing digital immunoassays for pTau181, pTau217, and pTau231 found strong correlations with amounts of pTau species in CSF. Moreover, although the fold change was highest for pTau217, the accuracy in identifying amyloid PET positivity was very high for all pTau species ([ 9 ][10]), suggesting that differences are not meaningful. A study of two large cohorts of 883 individuals with cognitive symptoms also showed high accuracy (90 to 91%) of both plasma pTau181 and pTau217 to predict clinical progression to AD dementia in algorithms that include memory and executive function tests and APOE genotyping ([ 10 ][11]). Overall, plasma pTau biomarkers fulfill many requirements for a clinically useful AD test, with a high fold change in AD (between two to four times higher in AD than non-AD controls across studies), and an increase early in the AD continuum (even preclinically), an association with amyloid-associated tau pathophysiology and tangle burden in the brain, and an increase specifically found in AD but not in other types of dementia. The findings of an early increase in plasma pTau fragments in patients with evidence of amyloid plaques, but not tau abnormalities, by PET imaging may be interpreted as a neuronal response to Aβ aggregates that gives rise to increased pTau secretion into CSF and blood plasma. However, findings in biomarker studies are only associations and may not directly reveal causal relationships. For example, plasma pTau231 shows a 10- to 15-fold increase within 24 hours after acute traumatic brain injury, especially evident in younger patients (who are unlikely to have amyloid or tau pathology) ([ 11 ][12]). Total tau (T-tau), referring to any tau variant or fragment regardless of phosphorylation, and other brain proteins such as glial fibrillary acidic protein (GFAP) also increase in blood plasma, hypothetically mediated by a trauma-induced compromise of the blood-brain barrier, with release of proteins preexisting in the extracellular space. Even if different mechanisms operate in specific disorders, further research is needed to understand the mechanisms underlying the increase in plasma pTau in AD. In the search for blood biomarkers of neurodegeneration, it has become evident that in contrast to CSF, where T-tau is markedly increased in AD, T-tau does not work as a biomarker of AD neurodegeneration in blood. Instead, another axonal protein, neurofilament light (NFL), has been evaluated as a substitute AD neurodegeneration biomarker, even though it is not involved in AD pathogenesis. Plasma NFL concentrations correlate well with CSF concentrations, supporting that it reflects brain pathophysiology. But high amounts are found in a wide variety of neurodegenerative disorders, so this biomarker lacks specificity. Nevertheless, plasma NFL, which shows a modest increase in AD, predicts both cognitive deterioration and rate of neurodegeneration as measured by atrophy on brain imaging. Notably, both plasma and CSF NFL concentrations increase in cognitively unimpaired people with autosomal dominant AD 7 years before symptom onset ([ 12 ][13]), so this may be a good biomarker for predicting AD. Another candidate AD blood biomarker includes the astrocytic protein GFAP, which is markedly increased in AD. Plasma GFAP distinguishes amyloid PET-positive and -negative cognitively normal elderly with high accuracy ([ 13 ][14]), and may serve as a blood biomarker for glial activation and neuroinflammation. Despite both rapid and robust reductions in amyloid PET ligand binding after treatment with Aβ immunotherapies (indicative of drug target engagement), effects on cognitive outcomes have been less evident. Therefore, biomarker evidence for downstream effects on reducing tau pathology and neurodegeneration is important to support disease-modifying effects by this class of drugs. Given that in most clinical trials only a small percentage of enrolled patients undergo repeat lumbar puncture for CSF testing, blood biomarkers could play an important role to accomplish this. Data from other areas of clinical neuroscience show that children with spinal muscular atrophy have a marked increase in CSF NFL, but treatment with the antisense oligonucleotide drug nusinersen results in a successive reduction of NFL concentrations in CSF with normalization after ∼7 months, and the reduction correlates with clinical improvements ([ 14 ][15]). Similar, but less pronounced, reductions of plasma NFL are seen with disease-modifying treatments in multiple sclerosis patients. These findings may serve as proof of concept for the usefulness of plasma NFL in identifying downstream drug effects on neurodegeneration. Target engagement for the anti-Aβ drug, aducanumab, was demonstrated in 2017, with dose-dependent reductions on amyloid PET ([ 15 ][16]), but to date there are no reports of effects on blood biomarkers of neurodegeneration (or tau pathology) from any Aβ immunotherapy trial. Current studies of blood AD biomarkers come exclusively from cohorts at highly specialized research centers. Thus, further clinical validation is needed, specifically on the diagnostic accuracy of the AD blood biomarkers, alone or in combination, in consecutive patient populations at memory clinics and in primary care settings. In addition, because plasma pTau increases progressively with tau pathology in the brain and more advanced clinical stage, more data are needed on the accuracy of plasma pTau biomarkers to identify individuals with preclinical or early symptoms who will go on to develop AD. Moreover, studies comparing plasma pTau species in the same cohorts and using the same technology are needed to understand if there are pathophysiological differences across the pTau epitopes. Current assays are research grade, and full analytical validation of methods is needed to achieve accurate and comparable results between laboratories, as well as global efforts to develop certified reference materials to achieve harmonization across assay platforms. Transferring the blood tests to fully automated platforms would also help to streamline these procedures and to establish these blood tests as clinically useful tools. Lastly, to make blood biomarkers attractive substitutes for imaging, costs need to be substantially lower than costs for the PET scans. 1. [↵][17]1. A. Nakamura et al ., Nature 554, 249 (2018). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. A. Keshavan et al ., Brain 144, 434 (2021). [OpenUrl][22] 3. [↵][23]1. S. E. Schindler et al ., Neurology 93, 17 (2019). 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Olsson et al ., J. Neurol. 266, 2129 (2019). [OpenUrl][50] 15. [↵][51]1. J. Sevigny et al ., Nature 546, 564 (2017). [OpenUrl][52] Acknowledgments: K.B. has consulted for Axon, Biogen, Lilly, and Roche Diagnostics and is cofounder of Brain Biomarker Solutions in Gothenburg AB. 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We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches, which we assess for predictive performance and important medical AI properties such as interpretability, uncertainty reasoning, data-efficiency, and leveraging domain knowledge. Our Bayesian approach combines the flexibility of Gaussian processes with the structural power of neural networks to model biomarker progressions, without needing clinical labels for training. We run evaluations on the problem of Alzheimer's disease prediction, yielding results surpassing deep learning and with the practical advantages of Bayesian non-parametrics and probabilistic programming.

Despite the wealth and quality of basic neuroscience research, there is still little we can do to treat or prevent most brain disorders. Industry efforts, meanwhile, have shied away from this field, particularly after a series of major drug candidates for the treatment of Alzheimer's disease failed to meet expectations ([ 1 ][1]). My previous research, which entailed developing and using optogenetics ([ 2 ][2], [ 3 ][3]) to understand how deep brain stimulation works in Parkinson's disease (PD) ([ 4 ][4], [ 5 ][5]), resulted in two key insights: We need to look and intervene earlier in brain disease progression, and we need to be able to access relevant cell populations with noninvasive yet precise tools to investigate, prevent, contain, or even reverse the course of disease. Accumulating evidence has highlighted a third insight: We may need to look beyond the brain to fully understand brain disorders ([ 6 ][6], [ 7 ][7]). My goal has been to develop an effective toolkit for neuromodulation so we can start to bridge the gap between what we know and what we can do to treat the brain. To achieve minimally invasive optogenetic-mediated modulation, we need to be able to penetrate the blood–brain barrier (BBB) so that vectors can be delivered systemically rather than through intracranial injections and address the poor reach of visible light through tissue so that large tissue volumes can be recruited without implantation of optical fibers. For early intervention, we need to get past the neuronal and brain-centric view of neurological disease. The adult brain is protected from compounds circulating in the blood by the BBB. Gene delivery to the brain requires surgery that is not only invasive but also results in limited tissue coverage and nonuniform gene expression. To achieve sufficient coverage for conditions characterized by broad dysfunction, such as neurodegeneration, multiple injections are needed, each of which creates local inflammation and damage. Systemic delivery would therefore be preferable to focal delivery because it does not require surgery and achieves broader tissue coverage. We have pioneered a powerful strategy that allows for the generation and selection of adeno-associated viral (AAV) vectors with optimized properties through Cre-recombination-dependent AAV-targeted evolution (CREATE) ([ 8 ][8]). My lab has used CREATE to synthesize AAVs that cross the BBB and transduce most cells in the adult mouse brain (see the first figure). These systemically delivered AAVs enable noninvasive brainwide transduction of specific cell types and regions in rodents when used with gene regulatory elements ([ 9 ][9]). During our quest to achieve systemic delivery of AAVs with opsin cargoes, we learned that the lower per-cell transgene copy number produced by systemic delivery led to ineffective overall opsin conductance. We needed better, high-performance opsins to make this method viable. To achieve such opsins, we built diversity into channelrhodopsin (ChR) using structure-guided SCHEMA ([ 10 ][10]) protein recombination from distinct opsin parents and then measured membrane localization and photocurrents. However, the dominant method for testing opsin properties—whole-cell patch clamping—has low throughput, so we used machine learning with limited training data to efficiently explore the vast sequence space and restrict the number of opsins to be tested. With Gaussian-process models trained on a limited experimental set of 102 functionally characterized ChRs, we selected ChR sequences that the models predicted would express, localize, and function. The result was a panel of high-photocurrent ChRs with exceptional light sensitivity (ChRgers) ([ 11 ][11]). These high-fluxing opsins not only overcome the low–copy number limitation of systemic delivery but also allow the light source to be placed at a distance from the transduced cells (for example, on a thinned mouse skull rather than implanted directly in the brain). The net effect of these advances is more effective coverage by both light and transgenes. This may be particularly useful for advancing optogenetic studies in nonhuman primates (NHPs), a key model relevant to human health. Optogenetics has had a relatively limited impact in NHP research, compared to its impact in smaller model organisms, predominantly owing to coverage problems stemming from the delivery limitations for genes and photons. Using systemic delivery of high-performance opsins to transduce and recruit large brain regions in NHPs could enable better neuromodulation in these important animal models. Neurodegeneration research has focused mainly on compromised neurons and circuits in the brain. Nevertheless, evidence points to roles for inflammation mediated by non-neuronal brain cells and body-to-brain connections (by way of the peripheral nervous system and/or a compromised BBB) ([ 6 ][6], [ 7 ][7]). Engineering gene-delivery tools that specifically target non-neuronal brain cells relevant to neurodegeneration, such as the brain endothelial cells that constitute the vasculature and the BBB, may be paradigm shifting because an impaired BBB can initiate and/or precipitate neurodegeneration. The ability to perform both positive and negative selection is key to yielding vectors with desired organ, cell type, and cell region tropisms. ![Figure][12] Multiplexed CREATE (Cre-recombination–based AAV-targeted evolution) Engineered systemic AAV capsids with improved tropism for peripheral sensory neurons (AAV-PHP.S, left) or the central nervous system (AAV-PHP.B and AAV-PHP.eB, right) upon systemic delivery in adult mice. Panels show native eGFP (enchanced green fluorescent protein) in the intact brain. Scale bar represents 1 mm. GRAPHIC: ADAPTED FROM CHAN ET AL. ([ 9 ][9]) BY N. DESAI/ SCIENCE To enable this, we transformed our CREATE platform into multiplexed M-CREATE ([ 12 ][13]), an in vivo screening strategy that incorporates next-generation sequencing, synthetic library generation, and a dedicated analysis pipeline. Using M-CREATE, we identified capsid variants that exhibited bias toward vascular cells or that targeted neurons with greater specificity, as well as capsids that transduced the central nervous system broadly or crossed the BBB in diverse murine strains. As a weak BBB can allow pathological factors into the brain ([ 13 ][14], [ 14 ][15]), functionally targeting BBB permeability by means of engineered AAVs can affect body-to-brain access through the circulation. Modulating permeability affords the opportunity to study and/or repair a barrier that can be weakened in disease or to deliver therapies to the brain by way of the bloodstream. Synucleinopathies are neurodegenerative diseases characterized by the aggregation of insoluble amyloid α-synuclein (αSyn) fibrils. PD is a synucleinopathy characterized by death of selected midbrain and brainstem neuronal populations and motor dysfunction. Roughly 90% of Parkinson's cases arise sporadically, making the study of its etiology difficult. Emerging findings suggest that nonmotor features such as loss of smell and gastrointestinal deficits may precede clinical diagnosis ([ 15 ][16], [ 16 ][17]). Postmortem biopsies from asymptomatic PD-diagnosed individuals have revealed the presence of pathologic αSyn assemblies in gastrointestinal tissue, leading Braak and colleagues to suggest that αSyn aggregation may originate in peripheral tissues such as the gut and progress to the brain by way of autonomic fibers ([ 17 ][18], [ 18 ][19]). Understanding the role of the peripheral nervous system in propagating pathology may therefore aid our understanding of neurodegeneration and help prevent it. Recognizing the lack of tools and methods available to study peripheral nervous systems, we developed whole-body tissue clearing and a tunable and rapid vector expression system that we used to evaluate network connectivity in the enteric nervous system (ENS) ([ 19 ][20], [ 20 ][21]). We injected a modest amount of αSyn preformed fibrils into the gut lining of mice (specifically the highly innervated duodenal wall) and observed subsequent gastrointestinal inflammation and physiological changes to the ENS (measured by optogenetics, calcium imaging, and changes in fecal production) ([ 21 ][22]). ENS pathology was also associated with a severe deficit in the lysosomal enzyme glucocerebrosidase, encoded by the gene GBA1 , known to be involved in Gaucher disease and PD. We therefore delivered GBA1 by means of the AAV-PHP.S capsid, which efficiently transduces the peripheral nervous system, to noninvasively restore glucocerebrosidase in the periphery. This led to a reduction in αSyn pathology and hints at a possible therapeutic target for early PD. Lastly, we demonstrated that inoculation of αSyn fibrils in aged mice, but not younger mice, resulted in progression of αSyn histopathology to the midbrain and decreased dopamine in the striatum, and subsequent motor defects. Taken together, this work (summarized in the second figure) shifts the focus of neurodegenerative disease etiology to the peripheral nervous system and expands our understanding of the role played by the ENS in prodromal synucleinopathy. ![Figure][12] αSyn fibrils can disrupt the enteric nervous system and aging increases susceptibility to the progression of αSyn pathology from the gut to the brain, leading to motor dysfunction. These deleterious effects were mitigated by peripheral gene transfer of GBA1 , through the systemic administration of specially engineered AAVs. GRAPHIC: ADAPTED FROM CHALLIS ET AL. ([ 21 ][22]) AND CHAN ET AL. ([ 9 ][9]) BY N. DESAI/ SCIENCE We aim to bridge the gap between what is currently feasible in neuromodulation and what is needed to meaningfully improve the lives of those with neuropathologies. We have used protein engineering principles to noninvasively, effectively, and specifically deliver effector genomes to nervous tissues and associated cell types ([ 22 ][23]). We have made advances in the creation of systemic viruses that cross the BBB, which open up the potential for noninvasive modulation of targets deep in the brain. In addition, we have used our gene delivery and optogenetic tools to modulate the peripheral nervous system in a mouse model of PD, demonstrating the potential utility of neuromodulation beyond the brain for the treatment of brain disorders. A number of relevant barriers remain, including the need to expand the AAV modest packaging limit, to penetrate the BBB in a variety of species, and to carefully consider and mitigate AAV side effects with better vectors, delivery methods, and immune avoidance strategies. Nevertheless, the findings and resources generated by this work represent a step forward with implications for neurological disorders and will be generalizable across neurological and psychiatric indications. GRAND PRIZE WINNER Viviana Gradinaru Viviana Gradinaru received her B.S. from the California Institute of Technology (CalTech) and a Ph.D. in neuroscience from Stanford Medical School. After a year in industry, Gradinaru started her lab in the Division of Biology and Biological Engineering at Caltech in 2012, where she is now a professor of neuroscience and biological engineering. Gradinaru's research group specializes in developing neuroscience tools and methods, including engineering of microbial opsins and viral vectors with optimized brain tropism with systemic delivery. Her research uses mouse models of neurodevelopmental and neurodegenerative disorders combined with electrophysiology and optogenetics to understand the cellular basis of dysfunction with the goal of developing new strategies for intervention. FINALIST Guosong Hong Guosong Hong received his undergraduate degree from Peking University and a Ph.D. from Stanford University. After completing his postdoctoral fellowship at Harvard University, Guosong started his lab in the Department of Materials Science and Engineering at Stanford University in 2018. His research aims to develop new materials-enabled neurotechnologies to interrogate and manipulate the brain with high spatiotemporal resolution, minimal invasiveness, and targeted neural specificity. [ www.sciencemag.org/content/369/6504/638 ][24] 1. [↵][25]1. K. Servick , Science 10.1126/science.aax4236 (2019). 2. [↵][26]1. V. Gradinaru et al ., Cell 141, 154 (2010). [OpenUrl][27][CrossRef][28][PubMed][29][Web of Science][30] 3. [↵][31]1. V. 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