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1. A taxonomy of the brain’s white matter: twenty-one major tracts for the 21st century

   https://doi.org/10.1093/cercor/bhab500  

   Bullock, Daniel N.; Hayday, Elena A.; Grier, Mark D.; Tang, Wei; Pestilli, Franco; Heilbronner, Sarah R. (February 2022, Cerebral Cortex)

   Abstract The functional and computational properties of brain areas are determined, in large part, by their connectivity profiles. Advances in neuroimaging and network neuroscience allow us to characterize the human brain noninvasively, but a comprehensive understanding of the human brain demands an account of the anatomy of brain connections. Long-range anatomical connections are instantiated by white matter, which itself is organized into tracts. These tracts are often disrupted by central nervous system disorders, and they can be targeted by neuromodulatory interventions, such as deep brain stimulation. Here, we characterized the connections, morphology, traversal, and functions of the major white matter tracts in the brain. There are major discrepancies across different accounts of white matter tract anatomy, hindering our attempts to accurately map the connectivity of the human brain. However, we are often able to clarify the source(s) of these discrepancies through careful consideration of both histological tract-tracing and diffusion-weighted tractography studies. In combination, the advantages and disadvantages of each method permit novel insights into brain connectivity. Ultimately, our synthesis provides an essential reference for neuroscientists and clinicians interested in brain connectivity and anatomy, allowing for the study of the association of white matter’s properties with behavior, development, and disorders. 

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2. The connectome of an insect brain

   https://doi.org/10.1126/science.add9330  

   Winding, Michael; Pedigo, Benjamin D.; Barnes, Christopher L.; Patsolic, Heather G.; Park, Youngser; Kazimiers, Tom; Fushiki, Akira; Andrade, Ingrid V.; Khandelwal, Avinash; Valdes-Aleman, Javier; et al (March 2023, Science)

   INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function. 

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3. POTION : Optimizing Graph Structure for Targeted Diffusion

   Yu, Sixie; Torres, Leo; Alfeld, Scott; Eliassi-Rad, Tina; Vorobeychik, Yevgeniy (January 2021, SIAM Data Mining Conference)

   null (Ed.)

   The problem of diffusion control on networks has been extensively studied, with applications ranging from marketing to controlling infectious disease. However, in many applications, such as cybersecurity, an attacker may want to attack a targeted subgraph of a network, while limiting the impact on the rest of the network in order to remain undetected. We present a model POTION in which the principal aim is to optimize graph structure to achieve such targeted attacks. We propose an algorithm POTION-ALG for solving the model at scale, using a gradient-based approach that leverages Rayleigh quotients and pseudospectrum theory. In addition, we present a condition for certifying that a targeted subgraph is immune to such attacks. Finally, we demonstrate the effectiveness of our approach through experiments on real and synthetic networks. 

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4. Pattern of Altered Magnetization Transfer Rate in Alzheimer’s Disease

   https://doi.org/10.3233/JAD-220335  

   Duan, Wenna; Sehrawat, Parshant; Zhou, Tony D.; Becker, James T.; Lopez, Oscar L.; Gach, H. Michael; Dai, Weiying (July 2022, Journal of Alzheimer's Disease)

   Background: Biomarkers for Alzheimer’s disease (AD) are crucial for early diagnosis and treatment monitoring once disease modifying therapies become available. Objective: This study aims to quantify the forward magnetization transfer rate (kfor) map from brain tissue water to macromolecular protons and use it to identify the brain regions with abnormal kfor in AD and AD progression. Methods: From the Cardiovascular Health Study (CHS) cognition study, magnetization transfer imaging (MTI) was acquired at baseline from 63 participants, including 20 normal controls (NC), 18 with mild cognitive impairment (MCI), and 25 AD subjects. Of those, 53 participants completed a follow-up MRI scan and were divided into four groups: 15 stable NC, 12 NC-to-MCI, 12 stable MCI, and 14 MCI/AD-to-AD subjects. kfor maps were compared across NC, MCI, and AD groups at baseline for the cross-sectional study and across four longitudinal groups for the longitudinal study. Results: We found a lower kfor in the frontal gray matter (GM), parietal GM, frontal corona radiata (CR) white matter (WM) tracts, frontal and parietal superior longitudinal fasciculus (SLF) WM tracts in AD relative to both NC and MCI. Further, we observed progressive decreases of kfor in the frontal GM, parietal GM, frontal and parietal CR WM tracts, and parietal SLF WM tracts in stable MCI. In the parietal GM, parietal CR WM tracts, and parietal SLF WM tracts, we found trend differences between MCI/AD-to-AD and stable NC. Conclusion: Forward magnetization transfer rate is a promising biomarker for AD diagnosis and progression. 

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5. Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures

   https://doi.org/10.1101/2025.04.29.651327  

   Tentori, Elisa; Kastellakis, George; Maschietto, Marta; Leparulo, Alessandro; Poirazi, Panayiota; Mazzucato, Luca; Allegra, Michele; Vassanelli, Stefano (May 2025, bioRxiv)

   ABSTRACT Achieving targeted perturbations of neural activity is essential for dissecting the causal architecture of brain circuits. A crucial challenge in targeted manipulation experiments is the identification ofhigh efficacyperturbation sites whose stimulation exerts desired effects, currently done with costly trial-and-error procedures. Can one predict stimulation effects solely based on observations of the circuit activity, in the absence of perturbation? We answer this question in dissociated neuronal cultures on High-Density Microelectrode Arrays (HD-MEAs), which, compared toin vivopreparations, offer a controllablein vitroplatform that enables precise stimulation and full access to network dynamics. We first reconstruct theperturbome- the full map of network responses to focal electrical stimulation - by sequentially activating individual single sites and quantifying their network-wide effects. The measured perturbome patterns cluster into functional modules, with limited spread across clusters. We then demonstrate that the perturbome can be predicted from spontaneous activity alone. Using short baseline recordings in the absence of perturbations, we estimate Effective Connectivity (EC) and show that it predicts the spatial organization of the perturbome, including spatial clusters and local connectivity. Our results demonstrate that spontaneous dynamics encode the latent causal structure of neural circuits and that EC metrics can serve as effective, model-free proxies for stimulation outcomes. This framework enables data-driven targeting and causal inferencein vitro, with potential applications to more complex preparations such as human iPSC-derived neurons and brain organoids, with implications for both basic research and therapeutic strategies targeting neurological disorders. Significance StatementNeuronal cultures are increasingly used as controllable platforms to study neuronal network dynamics, neuromodulation, and brain-inspired therapies. To fully exploit their potential, we need robust methods to probe and interpret causal interactions. Here, we develop a framework to reconstruct the perturbome—the network-wide map of responses to localized electrical stimulation—and show that it can be predicted from spontaneous activity alone. Using simple, model-free metrics of Effective Connectivity, we reveal that ongoing activity encodes causal structure and provides reliable proxies for stimulation outcomes. This validates EC as a practical measure of causal influence in vitro. Our methodology refines the use of neuronal cultures for brain-on-a-chip approaches, and paves the way for data-driven neuromodulation strategies in human stem cell–derived neurons and brain organoids. 

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