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Elke maandagochtend om 9.30 uur Parijse tijd bespreken we opwindende nieuwe wetenschap van vorige week met collega's, studenten en iedereen die geïnteresseerd is in de hersenen.

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ID: 895 8114 6322, Code: CNS

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Paper link: https://www.nature.com/articles/s41586-024-07643-2 Abstract From sequences of speech sounds1,2 or letters3, humans can extract rich and nuanced meaning through language. This capacity is essential for human communication. Yet, despite a growing understanding of the brain areas that support linguistic and semantic processing4,5,6,7,8,9,10,11,12, the derivation of linguistic meaning in neural tissue at the cellular level and over the timescale of action potentials remains largely unknown. Here we recorded from single cells in the left language-dominant prefrontal cortex as participants listened to semantically diverse sentences and naturalistic stories. By tracking their activities during natural speech processing, we discover a fine-scale cortical representation of semantic information by individual neurons. These neurons responded selectively to specific word meanings and reliably distinguished words from nonwords. Moreover, rather than responding to the words as fixed memory representations, their activities were highly dynamic, reflecting the words’ meanings based on their specific sentence contexts and independent of their phonetic form. Collectively, we show how these cell ensembles accurately predicted the broad semantic categories of the words as they were heard in real time during speech and how they tracked the sentences in which they appeared. We also show how they encoded the hierarchical structure of these meaning representations and how these representations mapped onto the cell population. Together, these findings reveal a finely detailed cortical organization of semantic representations at the neuron scale in humans and begin to illuminate the cellular-level processing of meaning during language comprehension.

Paper link: https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(24)00160-1/abstract Despite substantial advances in cancer treatment, for patients with glioblastoma prognosis remains bleak. The emerging field of cancer neuroscience reveals intricate functional interplays between glioblastoma and the cellular architecture of the brain, encompassing neurons, glia, and vessels. New findings underscore the role of structural and functional connections within hierarchical networks, known as the connectome. These connections contribute to the location, spread, and recurrence of a glioblastoma, and a patient's overall survival, revealing a complex interplay between the tumour and the CNS. This mounting evidence prompts a paradigm shift, challenging the perception of glioblastomas as mere foreign bodies within the brain. Instead, these tumours are intricately woven into the structural and functional fabric of the brain. This radical change in thinking holds profound implications for the understanding and treatment of glioblastomas, which could unveil new prognostic factors and surgical strategies and optimise radiotherapy. Additionally, a connectivity approach suggests that non-invasive brain stimulation could disrupt pathological neuron–glioma interactions within specific networks.

The Human Cerebellum: A Digital Anatomical Atlas at the Level of Individual Folia John G. Samuelsson, Jeremy D. Schmahmann, Martin Sereno, Bruce Rosen, Matti S. Hämäläinen doi: https://doi.org/10.1101/2024.08.27.610006 Scientific interest in the cerebellum has surged in the last few decades with an emerging consensus on a multifaceted functionality and intricate, but not yet fully understood, functional topography over the cerebellar cortex. To further refine this structure-function relationship and quantify its inter-subject variability, a high-resolution digital anatomical atlas is fundamental. Using a combination of manual labeling and image processing, we turned a recently published reconstruction of the human cerebellum, the first such reconstruction fine enough to resolve the individual folia, into a digital atlas with both surface and volumetric representations. Its unprecedented granularity (0.16 mm) and detailed expert labeling make the atlas usable as an anatomical ground truth, enabling new ways of analyzing and visualizing cerebellar data through its digital format. Paper link: https://www.biorxiv.org/content/10.1101/2024.08.27.610006v1 Sereno 2020: https://www.pnas.org/doi/full/10.1073/pnas.2002896117 Other YouTUBe talks on the cerebellum: 1. Fibre dissection of deep cerebellar nuclei and cerebellar peduncles - Nupur Pruthi https://www.youtube.com/watch?v=J1ck8bg7z3c&t=493s 2. Fiber dissection technique: dissection of the brainstem - Carlo Serra https://www.youtube.com/watch?v=GHFqiH2u9uk&t=6s

Tractography from T1-weighted MRI: Empirically exploring the clinical viability of streamline propagation without diffusion MRI Paper link: https://doi.org/10.1162/imag_a_00259 Abstract: Over the last few decades, diffusion MRI (dMRI) streamline tractography has emerged as the dominant method for in vivo estimation of white matter (WM) pathways in the brain. One key limitation to this technique is that modern tractography implementations require high angular resolution diffusion imaging (HARDI). However, HARDI can be difficult to collect clinically, limiting the reach of tractography analyses to research cohorts and thus limiting many WM investigations to certain populations and pathologies. As such, a clinically viable tractography solution applicable to wider patient populations scanned as a part of routine care would be of key significance in broadening WM analyses to underfunded or rarer diseases and to the clinical setting. Such a solution would require the ability to perform arbitrary tractography analyses, use only clinical imaging for input, and be open source and widely accessible and implementable. Thus, here we evaluate our recently developed, containerized, and open-source, T1-weighted (T1w) MRI-based deep learning model for streamline propagation. We empirically assess its performance against traditional dMRI-based and established atlas-based approaches in a healthy young population, an aging one, and in those with epilepsy, depression, and brain cancer. In the healthy young population, we find slightly increased error compared to traditional tractography with the deep learning model that falls within the bounds attributable to dMRI variability and is considerably less than the atlas-based approach. Further, seeking to replicate previously published dMRI tractography effects in the remaining cohorts as an initial assessment of clinical viability, we find this model successfully does so in some key cases—particularly in applications that rely on long-range streamlines including those not captured by the atlas-based approach—but importantly not all. These results suggest a deep learning-based approach to tractography with T1w MRI demonstrates promise within the limitations of our definition of clinical viability and especially over atlas-based approaches but requires refinement and more robust consideration of out-of-distribution effects prior to widespread clinical use. We also find these results raise additional questions regarding the differences in image content between dMRI and T1w MRI and their relationship to tractography. Further investigation of these questions will improve the field’s understanding of which features of the brain influence measured tractography effects. diffusion MRI, T1-weighted MRI, streamline tractography, open-source software, white matter, convolutional recurrent neural networks, CoRNN, #Tractography from #T1-weighted #MRI: Empirically exploring the #clinical viability of streamline propagation without #diffusion MRI

Hemispheric dissociations in regions supporting auditory sentence comprehension in older adults Paper link: https://www.sciencedirect.com/science/article/pii/S2589958922000238?via%3Dihub Abstract We investigated how the aging brain copes with acoustic and syntactic challenges during spoken language comprehension. Thirty-eight healthy adults aged 54 – 80 years (M = 66 years) participated in an fMRI experiment wherein listeners indicated the gender of an agent in short spoken sentences that varied in syntactic complexity (object-relative vs subject-relative center-embedded clause structures) and acoustic richness (high vs low spectral detail, but all intelligible). We found widespread activity throughout a bilateral frontotemporal network during successful sentence comprehension. Consistent with prior reports, bilateral inferior frontal gyrus and left posterior superior temporal gyrus were more active in response to object-relative sentences than to subject-relative sentences. Moreover, several regions were significantly correlated with individual differences in task performance: Activity in right frontoparietal cortex and left cerebellum (Crus I & II) showed a negative correlation with overall comprehension. By contrast, left frontotemporal areas and right cerebellum (Lobule VII) showed a negative correlation with accuracy specifically for syntactically complex sentences. In addition, laterality analyses confirmed a lack of hemispheric lateralization in activity evoked by sentence stimuli in older adults. Importantly, we found different hemispheric roles, with a left-lateralized core language network supporting syntactic operations, and right-hemisphere regions coming into play to aid in general cognitive demands during spoken sentence processing. Together our findings support the view that high levels of language comprehension in older adults are maintained by a close interplay between a core left hemisphere language network and additional neural resources in the contralateral hemisphere.

Eva Guzmán Chacón, the first author, summarises her paper on embracing digital innovation in neuroscience: 2023 in review at Neuroccino. As we reflect on the past year in neuroscience, it is evident that the field has reached an exciting juncture of discovery and innovation, significantly influenced by technological advancements. The traditional avenues of knowledge dissemination, including conferences and lab discussions, have been effectively augmented by digital platforms, fostering global scientific engagement. A prime example of this evolution is the non-profit Clinical Neuroanatomy Seminars, which transitioned online in 2016, well ahead of the digital curve. This bold move, initially aimed at broadening access to scientific seminars, became particularly crucial during the COVID-19 pandemic, transforming our initiative into a pivotal global scientific platform. Keep on reading: https://link.springer.com/article/10.1007/s00429-024-02768-6 Please cite the paper: Embracing Digital Innovation in Neuroscience: 2023 in Review at Neuroccino. ​​Guzmán Chacón E, Ovando-Tellez M, Thiebaut de Schotten M, Forkel SJ, DOI : 10.1007/s00429-024-02768-6

Separate lanes for adding and reading in the white matter highways of the human brain Math and reading involve distributed brain networks and have both shared (e.g. encoding of visual stimuli) and dissociated (e.g. quantity processing) cognitive components. Yet, to date, the shared vs. dissociated gray and white matter substrates of the math and reading networks are unknown. Here, we define these networks and evaluate the structural properties of their fascicles using functional MRI, diffusion MRI, and quantitative MRI. Our results reveal that there are distinct gray matter regions which are preferentially engaged in either math (adding) or reading, and that the superior longitudinal and arcuate fascicles are shared across the math and reading networks. Strikingly, within these fascicles, reading- and math-related tracts are segregated into parallel sub-bundles and show structural differences related to myelination. These findings open a new avenue of research that examines the contribution of sub-bundles within fascicles to specific behaviors. Paper link: https://www.nature.com/articles/s41467-019-11424-1

Autonomic responses to emotional linguistic stimuli and amplitude of low-frequency fluctuations predict outcome after severe brain injury An accurate prognosis on the outcome of brain-injured patients with disorders of consciousness (DOC) remains a significant challenge, especially in the acute stage. In this study, we applied a multiple-technique approach to provide accurate predictions on functional outcome after 6 months in 15 acute DOC patients. Electrophysiological correlates of implicit cognitive processing of verbal stimuli and data-driven voxel-wise resting-state fMRI signals, such as the fractional amplitude of low-frequency fluctuations (fALFF), were employed. Event-related electrodermal activity, an index of autonomic activation, was recorded in response to emotional words and pseudo-words at baseline (T0). On the same day, patients also underwent a resting-state fMRI scan. Six months later (T1), patients were classified as outcome-negative and outcome-positive using a standard functional outcome scale. We then revisited the baseline measures to test their predictive power for the functional outcome measured at T1. We found that only outcome-positive patients had an earlier, higher autonomic response for words compared to pseudo-words, a pattern similar to that of healthy awake controls. Furthermore, DOC patients showed reduced fALFF in the posterior cingulate cortex (PCC), a brain region that contributes to autonomic regulation and awareness. The event-related electrodermal marker of residual cognitive functioning was found to have a significant correlation with residual local neuronal activity in the PCC. We propose that a residual autonomic response to cognitively salient stimuli, together with a preserved resting-state activity in the PCC, can provide a useful prognostic index in acute DOC. Paperlink: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397392/

Paper link: https://research.vu.nl/ws/portalfiles/portal/297552871/Helwegen_2023_Statistical_power_in_network_neuroscience.pdf

Paper link: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011288 Abstract Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce “all-in-one” visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.

Abstract Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration. Paper link: https://pubmed.ncbi.nlm.nih.gov/37808645/

Antiamyloid antibodies have been used to reduce cerebral amyloid-beta (Aβ) load in patients with Alzheimer’s disease. We applied focused ultrasound with each of six monthly aducanumab infusions to temporarily open the blood–brain barrier with the goal of enhancing amyloid removal in selected brain regions in three participants over a period of 6 months. The reduction in the level of Aβ was numerically greater in regions treated with focused ultrasound than in the homologous regions in the contralateral hemisphere that were not treated with focused ultrasound, as measured by fluorine-18 florbetaben positron-emission tomography. Cognitive tests and safety evaluations were conducted over a period of 30 to 180 days after treatment. (Funded by the Harry T. Mangurian, Jr. Foundation and the West Virginia University Rockefeller Neuroscience Institute.) Paper link: https://www.nejm.org/doi/full/10.1056/NEJMoa2308719

Mary, Peter. Revision of the question of aphasia: the left third frontal gyrus plays no special role in language function. In: Medical Week, 1906, 26. pp. 241-247 Paper link: https://www.biusante.parisdescartes.fr/histoire/medica/resultats/index.php?do=page&cote=annee190606&p=1

Paper link: https://www.nature.com/articles/s41562-024-01852-5 Salient objects often capture our attention, serving as distractors and hindering our current goals. It remains unclear when and how salient distractors interact with our goals, and our knowledge on the neural mechanisms responsible for attentional capture is limited to a few brain regions recorded from non-human primates. Here we conducted a multivariate analysis on human intracranial signals covering most brain regions and successfully dissociated distractor-specific representations from target-arousal signals in the high-frequency (60–100 Hz) activity. We found that salient distractors were processed rapidly around 220 ms, while target-tuning attention was attenuated simultaneously, supporting initial capture by distractors. Notably, neuronal activity specific to the distractor representation was strongest in the superior and middle temporal gyrus, amygdala and anterior cingulate cortex, while there were smaller contributions from the parietal and frontal cortices. These results provide neural evidence for attentional capture by salient distractors engaging a much larger network than previously appreciated.

Paper link: https://www.nature.com/articles/s41928-023-01069-w Brain organoid reservoir computing for artificial intelligence Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework.

Causation in neuroscience: keeping mechanism meaningful A fundamental goal of research in neuroscience is to uncover the causal structure of the brain. This focus on causation makes sense, because causal information can provide explanations of brain function and identify reliable targets with which to understand cognitive function and prevent or change neurological conditions and psychiatric disorders. In this research, one of the most frequently used causal concepts is ‘mechanism’ — this is seen in the literature and language of the field, in grant and funding inquiries that specify what research is supported, and in journal guidelines on which contributions are considered for publication. In these contexts, mechanisms are commonly tied to expressions of the main aims of the field and cited as the ‘fundamental’, ‘foundational’ and/or ‘basic’ unit for understanding the brain. Despite its common usage and perceived importance, mechanism is used in different ways that are rarely distinguished. Given that this concept is defined in different ways throughout the field — and that there is often no clarification of which definition is intended — there remains a marked ambiguity about the fundamental goals, orientation and principles of the field. Here we provide an overview of causation and mechanism from the perspectives of neuroscience and philosophy of science, in order to address these challenges. Paper link: https://www.nature.com/articles/s41583-023-00778-7 #review

Paper link: https://pubmed.ncbi.nlm.nih.gov/37757883/ Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

Paper link: https://www.nature.com/articles/s41593-024-01570-1 Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourette’s syndrome, the supplementary motor area in Parkinson’s disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

More details: https://www.stephanieforkel.com/post/brainhack-padova-neuroscience-centre-pnc Brainhack: Diffusion & Tractography University of Padua, February 25-28th 2024 #tractography #diffusion #whitematter #anatomy

Paper link: https://arxiv.org/abs/2206.01653 Try their tool: https://metrics-reloaded.dkfz.de/ Abstract: Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases. Just published din @Nature Methods

Paper link: https://www.nature.com/articles/s41467-024-44824-z#code-availability Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans.

Paper link: https://www.pnas.org/doi/10.1073/pnas.2306286121

Paper link: https://elifesciences.org/articles/84716 Neurological insults, such Neurological insults, such as congenital blindness, deafness, amputation, and stroke, often result in surprising and impressive behavioural changes. Cortical reorganisation, which refers to preserved brain tissue taking on a new functional role, is often invoked to account for these behavioural changes. Here, we revisit many of the classical animal and patient cortical remapping studies that spawned this notion of reorganisation. We highlight empirical, methodological, and conceptual problems that call this notion into doubt. We argue that appeal to the idea of reorganisation is attributable in part to the way that cortical maps are empirically derived. Specifically, cortical maps are often defined based on oversimplified assumptions of ‘winner-takes-all’, which in turn leads to an erroneous interpretation of what it means when these maps appear to change. Conceptually, remapping is interpreted as a circuit receiving novel input and processing it in a way unrelated to its original function. This implies that neurons are either pluripotent enough to change what they are tuned to or that a circuit can change what it computes. Instead of reorganisation, we argue that remapping is more likely to occur due to potentiation of pre-existing architecture that already has the requisite representational and computational capacity pre-injury. This architecture can be facilitated via Hebbian and homeostatic plasticity mechanisms. Crucially, our revised framework proposes that opportunities for functional change are constrained throughout the lifespan by the underlying structural ‘blueprint’. At no period, including early in development, does the cortex offer structural opportunities for functional pluripotency. We conclude that reorganisation as a distinct form of cortical plasticity, ubiquitously evoked with words such as ‘take-over’’ and ‘rewiring’, does not exist.

Software: https://www.neurodesk.org/ Paper link: https://www.nature.com/articles/s41592-023-02145-x datalad install https://github.com/OpenNeuroDatasets/ds002785.git

Paper links: https://doi.org/10.1038/s41586-023-06683-4 https://doi.org/10.1016/j.neuron.2023.09.043