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Jie Mei

Assistant Professor of Computational Neuroscience

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Publications

TitleYear
Sepsis-associated cognitive dysfunction: An investigation using stress-free, automated behavioral tests
2025
Jie Mei
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Authors:Jie Mei
Publication date:05/05/2025
Description:Überlebende von chirurgischen und konservativen Intensivstationen haben ein hohes Risiko für lang anhaltende kognitive Defizite. Die Sepsis gilt als häufigste und schwerwiegendste der kritischen Erkrankungen, die zu kognitiven Defiziten führen können. Einer von drei Überlebenden einer Sepsis wird mit schwerer, neu aufgetretener kognitiver Dysfunktion aus dem Krankenhaus entlassen. Chronische Neuroinflammation, diffusive zerebrale Schädigungen und neuronaler Zelltod werden als die primären Korrelate in der Entwicklung lang anhaltender kognitiver Defizite angesehen. Neuere Studien deuten darauf hin, dass Sepsis eine inflammatorische Aktivierung von Mikroglia auslöst, die zu Phagozytose gestresster, aber funktionsfähiger Neurone führt. Um ein Mausmodell der Sepsis zu etablieren, wurden Lipopolysaccharide (LPS) in C57BL/6-Mäuse und homozygote Knockout- und Wildtyp-Mäuse mit Mertk-, CD11b- und Mfge8-Defizienz in einer Dosierung von 1,5mg/kg injiziert. Das akute Krankheitsverhalten und langzeitige kognitive Funktionen der Tiere wurden analysiert, um die Effekte von phagozytotischer Defizienz und Behandlung mit Peptiden auf kognitive Defizite zu untersuchen. Um die Anforderungen an das Tierwohl durch Reduzierung von Handling, Wasser- und/oder Nahrungsentzug und unnötigem Leid optimal zu erfüllen, untersuchten wir außerdem die Anwendbarkeit eines vollständig automatisierten 8-Arm-Radial Arm Mazes und von machine learning-basierter Bestimmung von Abbruchkriterien (humane endpoints). Tiere mit LPS-Injektion zeigten (a) Charakteristika von Krankheitsverhalten unmittelbar nach der Injektion …
Resources:[PDF] from fu-berlin.de
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Refining humane endpoints in mouse models of disease by systematic review and machine learning-based endpoint definition
2025
Jie Mei, Stefanie Banneke, Janet Lips, Melanie TC Kuffner, Christian J Hoffmann, Ulrich…
ALTEX: Alternatives to Animal Experimentation 36
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Authors:Jie Mei, Stefanie Banneke, Janet Lips, Melanie TC Kuffner, Christian J Hoffmann, Ulrich Dirnagl, Matthias Endres, Christoph Harms, Julius V Emmrich
Publication date:05/05/2025
Journal:ALTEX: Alternatives to Animal Experimentation
Volume:36
Description:Ideally, humane endpoints allow for early termination of experiments by minimizing an animal’s discomfort, distress and pain, while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off values published in the literature remain a challenge to the accurate determination and application of humane endpoints. With the aim to synthesize and appraise existing humane endpoint definitions for commonly used physiological parameters, we conducted a systematic review of mouse studies of acute and chronic disease models, which used body weight, temperature and/or sickness scores for endpoint definition. In the second part of the study, we used previously published and unpublished data on weight, temperature and sickness scores from mouse models of sepsis and stroke and applied machine learning algorithms to assess the usefulness of this method for parameter selection and endpoint definition across models. Studies were searched for in two electronic databases (MEDLINE/Pubmed and Embase). Out of 110 retrieved full-text manuscripts, 34 studies were included. We found large intra- and inter-model variance in humane endpoint determination and application due to varying animal models, lack of standardized experimental protocols and heterogeneity of performance metrics (part 1). Machine learning models trained with physiological data and sickness severity score or modified DeSimoni neuroscore identified animals with a high risk of death at an early time point in both mouse models of stroke (male: 93.2% at 72h post-treatment; female: 93.0% at 48h post-treatment) and …
Cited by:26
Resources:[PDF] from fu-berlin.de
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Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program
2025
Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz, Krzysztof Basiński, André M Bast…
arXiv preprint arXiv:2504.09614
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Authors:Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz, Krzysztof Basiński, André M Bastos, Michael Berry, Andres Canales-Johnson, Hannah Choi, Claudia Clopath, Uri Cohen, Rui Ponte Costa, Roberto De Filippo, Roman Doronin, Steven P Errington, Jeffrey P Gavornik, Colleen J Gillon, Arno Granier, Jordan P Hamm, Loreen Hertäg, Henry Kennedy, Sandeep Kumar, Alexander Ladd, Hugo Ladret, Jérôme A Lecoq, Alexander Maier, Patrick McCarthy, Jie Mei, Jorge Mejias, Fabian Mikulasch, Noga Mudrik, Farzaneh Najafi, Kevin Nejad, Hamed Nejat, Karim Oweiss, Mihai A Petrovici, Viola Priesemann, Lucas Rudelt, Sarah Ruediger, Simone Russo, Alessandro Salatiello, Walter Senn, Eli Sennesh, Sepehr Sima, Cem Uran, Anna Vasilevskaya, Julien Vezoli, Martin Vinck, Jacob A Westerberg, Katharina Wilmes, Yihan Sophy Xiong
Publication date:04/13/2025
Journal:arXiv preprint arXiv:2504.09614
Description:This review synthesizes advances in predictive processing within the sensory cortex. Predictive processing theorizes that the brain continuously predicts sensory inputs, refining neuronal responses by highlighting prediction errors. We identify key computational primitives, such as stimulus adaptation, dendritic computation, excitatory/inhibitory balance and hierarchical processing, as central to this framework. Our review highlights convergences, such as top-down inputs and inhibitory interneurons shaping mismatch signals, and divergences, including species-specific hierarchies and modality-dependent layer roles. To address these conflicts, we propose experiments in mice and primates using in-vivo two-photon imaging and electrophysiological recordings to test whether temporal, motor, and omission mismatch stimuli engage shared or distinct mechanisms. The resulting dataset, collected and shared via the OpenScope program, will enable model validation and community analysis, fostering iterative refinement and refutability to decode the neural circuits of predictive processing.
Resources:[PDF] from arxiv.org
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Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics
2025
Jie Mei, Alejandro Rodriguez-Garcia, Daigo Takeuchi, Gabriel Wainstein, Nina Hubig, Yal…
arXiv preprint arXiv:2501.06762
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Authors:Jie Mei, Alejandro Rodriguez-Garcia, Daigo Takeuchi, Gabriel Wainstein, Nina Hubig, Yalda Mohsenzadeh, Srikanth Ramaswamy
Publication date:01/12/2025
Journal:arXiv preprint arXiv:2501.06762
Description:Continuous, adaptive learning-the ability to adapt to the environment and improve performance-is a hallmark of both natural and artificial intelligence. Biological organisms excel in acquiring, transferring, and retaining knowledge while adapting to dynamic environments, making them a rich source of inspiration for artificial neural networks (ANNs). This study explores how neuromodulation, a fundamental feature of biological learning systems, can help address challenges such as catastrophic forgetting and enhance the robustness of ANNs in continuous learning scenarios. Driven by neuromodulators including dopamine (DA), acetylcholine (ACh), serotonin (5-HT) and noradrenaline (NA), neuromodulatory processes in the brain operate at multiple scales, facilitating dynamic responses to environmental changes through mechanisms ranging from local synaptic plasticity to global network-wide adaptability. Importantly, the relationship between neuromodulators, and their interplay in the modulation of sensory and cognitive processes are more complex than expected, demonstrating a "many-to-one" neuromodulator-to-task mapping. To inspire the design of novel neuromodulation-aware learning rules, we highlight (i) how multi-neuromodulatory interactions enrich single-neuromodulator-driven learning, (ii) the impact of neuromodulators at multiple spatial and temporal scales, and correspondingly, (iii) strategies to integrate neuromodulated learning into or approximate it in ANNs. To illustrate these principles, we present a case study to demonstrate how neuromodulation-inspired mechanisms, such as DA-driven reward processing and NA-based …
Cited by:2
Resources:[PDF] from arxiv.org
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Proportion of foetal and placental implantation abnormalities in Madagascar: A cross-sectional study of 35,919 women at public-sector primary healthcare facilities in central …
2024
Franziska Krätzig, Jie Mei, Mahery Rebaliha, Zavaniarivo Rampanjato, Rinja Ranaivoson, …
PloS one 19 (12), e0311918
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Authors:Franziska Krätzig, Jie Mei, Mahery Rebaliha, Zavaniarivo Rampanjato, Rinja Ranaivoson, Jenia Razafinjato, Jan-Walter De Neve, Mara Anna Franke, Nadine Muller, Julius Valentin Emmrich
Publication date:12/31/2024
Journal:PloS one
Volume:19
Issue:12
Pages:e0311918
Publisher:Public Library of Science
Description:Background Like other countries in sub-Saharan Africa, Madagascar has a high burden of maternal and neonatal mortality. However, as the proportion of foetal and placental abnormalities among the Malagasy population is unknown, strategies aimed at reducing maternal and neonatal mortality are challenging to define and implement. Methods We conducted a multi-year, cross-sectional study using secondary NGO data on obstetric ultrasound, including patient records of all pregnant women who received an obstetric ultrasound screening between July 1st, 2017, and September 30th, 2020, at 62 public-sector primary care facilities in urban and rural regions of Madagascar. We analysed demographic characteristics and determined the prevalence of foetal and placental abnormalities. Results The dataset included 38,688 ultrasound screening reports from 35,919 women, where 2,587/35,919 (7.20%) women had more than one ultrasound exam. Most women (68.63%, 26,550/38,688) received their first ultrasound during the third trimester of pregnancy. Foetal malpresentation at 36 weeks of gestation or later was diagnosed in 5.48% (176/3,211) of women with the breech presentation being most common (breech 3.99%, 128/3,211; transverse 0.84%, 27/3,211; mobile 0.5%, 16/3,211; oblique 0.16%, 5/3,211). Placenta previa was found in 2.31% (875/38,755) and multiple gestations in 1.03% (370/35,919) cases. Around one in every 150 women (0.66%, 234/38,702) had amniotic fluid disorders. Conclusion The proportion of foetal and placental abnormalities detected by obstetric ultrasound is consistent with findings from other countries in sub …
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Look-ahead selective plasticity for continual learning of visual tasks
2024
Rouzbeh Meshkinnejad, Jie Mei, Daniel Lizotte, Yalda Mohsenzadeh
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Authors:Rouzbeh Meshkinnejad, Jie Mei, Daniel Lizotte, Yalda Mohsenzadeh
Publication date:10/10/2024
Cited by:2
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Action plan diversity in children during control exploration: Link between action and sense of agency
2024
Wen Wen, Hakan Aktaş, Acer Yu-Chan Chang, Jie Mei, Yosuke Suzuishi, Yukie Nagai, Satosh…
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Authors:Wen Wen, Hakan Aktaş, Acer Yu-Chan Chang, Jie Mei, Yosuke Suzuishi, Yukie Nagai, Satoshi Nobusako
Publication date:10/04/2024
Publisher:OSF
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Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling
2024
Alejandro Rodriguez-Garcia, Jie Mei, Srikanth Ramaswamy
arXiv preprint arXiv:2407.04525
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Authors:Alejandro Rodriguez-Garcia, Jie Mei, Srikanth Ramaswamy
Publication date:07/05/2024
Journal:arXiv preprint arXiv:2407.04525
Description:Recent progress in artificial intelligence (AI) has been driven by insights from neuroscience, particularly with the development of artificial neural networks (ANNs). This has significantly enhanced the replication of complex cognitive tasks such as vision and natural language processing. Despite these advances, ANNs struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly. Specifically, ANNs often overlook the functional and morphological diversity of the brain, hindering their computational capabilities. Furthermore, incorporating cell-type specific neuromodulatory effects into ANNs with neuronal heterogeneity could enable learning at two spatial scales: spiking behavior at the neuronal level, and synaptic plasticity at the circuit level, thereby potentially enhancing their learning abilities. In this article, we summarize recent bio-inspired models, learning rules and architectures and propose a biologically-informed framework for enhancing ANNs. Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors and dendritic compartments to simulate morphological and functional diversity of neuronal computations. Finally, we outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, balances bioinspiration and complexity, and provides scalable solutions for pressing AI challenges, such as continual learning, adaptability, robustness, and resource-efficiency.
Resources:[PDF] from arxiv.org
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Control over self and others’ face: exploitation and exploration
2024
Wen Wen, Jie Mei, Hakan Aktas, Acer Yu-Chan Chang, Yosuke Suzuishi, Shunichi Kasahara
Scientific Reports 14 (1), 15473
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Authors:Wen Wen, Jie Mei, Hakan Aktas, Acer Yu-Chan Chang, Yosuke Suzuishi, Shunichi Kasahara
Publication date:07/05/2024
Journal:Scientific Reports
Volume:14
Issue:1
Pages:15473
Publisher:Nature Publishing Group UK
Description:The face serves as a crucial cue for self-identification, while the sense of agency plays a significant role in determining our influence through actions in the environment. The current study investigates how self-identification through facial recognition may influence the perception of control via motion. We propose that self-identification might engender a belief in having control over one’s own face, leading to a more acute detection and greater emphasis on discrepancies between their actions and the sensory feedback in control judgments. We refer to the condition governed by the belief in having control as the exploitation mode. Conversely, when manipulating another individual’s face, the belief in personal control is absent. In such cases, individuals are likely to rely on the regularity between actions and sensory input for control judgments, exhibiting behaviors that are exploratory in nature to glean such information …
Cited by:3
Resources:[HTML] from nature.com
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Understanding Context Dependence in Time Perception in Autism Spectrum Disorders: A Computational Study
2024
Jie Mei, Yukie Nagai
, 1-7
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Authors:Jie Mei, Yukie Nagai
Publication date:05/20/2024
Pages:1-7
Publisher:IEEE
Description:Previously acquired knowledge (e.g., prior) plays a crucial role in perceptual decision making. When estimating quantities, an integration of sensory estimates with acquired internal representations of quantities of similar nature may occur, leading to a bias toward the mean of these quantities. In autism spectrum disorders (ASD), sensory and perceptual atypicalities are observed across behavioral tasks. While many of these atypicalities can be explained by an attenuated influence of previously acquired knowledge, a few studies have reported a stronger effect of prior knowledge on perceptual decision making. Given these mixed experimental results, in the present study, we use a recurrent neural network to simulate and investigate perceptual decisions in ASD in a time perception task. In particular, we examine the effects of reliance on priors on interval reproduction performance, and attempt to provide a model …
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Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task
2023
Jie Mei, Rouzbeh Meshkinnejad, Yalda Mohsenzadeh
Iscience 26 (2)
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Authors:Jie Mei, Rouzbeh Meshkinnejad, Yalda Mohsenzadeh
Publication date:02/17/2023
Journal:Iscience
Volume:26
Issue:2
Publisher:Elsevier
Description:In recent years, the biological underpinnings of adaptive learning have been modeled, leading to faster model convergence and various behavioral benefits in tasks including spatial navigation and cue-reward association. Furthermore, studies have investigated how the neuromodulatory system, a major driver of synaptic plasticity and state-dependent changes in the brain neuronal activities, plays a role in training deep neural networks (DNNs). In this study, we extended previous studies on neuromodulation-inspired DNNs and explored the effects of neuromodulatory components on learning and single unit activities in a spatial learning task. Under the multiscale neuromodulatory framework, plastic components, dropout probability modulation, and learning rate decay were added to the single unit, layer, and whole network levels of DNN models, respectively. We observed behavioral benefits including faster …
Cited by:8
Resources:[HTML] from cell.com
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Assessing spatial learning and memory in mice: Classic radial maze versus a new animal-friendly automated radial maze allowing free access and not requiring food deprivation
2022
Joel Kohler, Jie Mei, Stefanie Banneke, York Winter, Matthias Endres, Julius Valentin E…
Frontiers in behavioral neuroscience 16, 1013624
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Authors:Joel Kohler, Jie Mei, Stefanie Banneke, York Winter, Matthias Endres, Julius Valentin Emmrich
Publication date:09/30/2022
Journal:Frontiers in behavioral neuroscience
Volume:16
Pages:1013624
Publisher:Frontiers Media SA
Description:The radial arm maze (RAM) is a common behavioral test to quantify spatial learning and memory in rodents. Prior attempts to refine the standard experimental setup have been insufficient. Previously, we demonstrated the feasibility of a fully automated, voluntary, and stress-free eight-arm RAM not requiring food or water deprivation. Here, we compared this newly developed refined RAM to a classic manual experimental setup using 24 female 10–12 weeks old C57BL/6J mice. We used a lipopolysaccharide (LPS)-induced model of systemic inflammation to examine long-term cognitive impairment for up to 13 weeks following LPS injection. Both mazes demonstrated robust spatial learning performance during the working memory paradigm. The refined RAM detected spatial learning and memory deficits among LPS-treated mice in the working memory paradigm, whereas the classic RAM detected spatial learning and memory deficits only in the combined working/reference memory paradigm. In addition, the refined RAM allowed for quantification of an animal’s overall exploratory behavior and day/night activity pattern. While our study highlights important aspects of refinement of the new setup, our comparison of methods suggests that both RAMs have their respective merits depending on experimental requirements.
Cited by:10
Resources:[HTML] from frontiersin.org
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Depression detection: text augmentation for robustness to label noise in self-reports
2022
Javed Ali, Dat Quoc Ngo, Aninda Bhattacharjee, Tannistha Maiti, Tarry Singh, Jie Mei
Digital Humanism: A Human-Centric Approach to Digital Technologies, 81-103
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Authors:Javed Ali, Dat Quoc Ngo, Aninda Bhattacharjee, Tannistha Maiti, Tarry Singh, Jie Mei
Publication date:06/29/2022
Journal:Digital Humanism: A Human-Centric Approach to Digital Technologies
Pages:81-103
Publisher:Springer International Publishing
Description:With a high prevalence in both high- and low-to-middle-income countries, depression is one of the world’s most common mental disorders, placing heavy burdens on society. Depression severely impairs the daily functioning and quality of life of individuals of different ages and may eventually lead to self-harm and suicide. Advancements have emerged in the fields of natural language for depression assessment, using bidirectional encoder representation from transformers (BERT). In this study, we used the Reddit Self-reported Depression Diagnosis dataset, an uncurated text-based dataset, to enable the detection of depression using easily accessible data and without the intervention of domain experts. To reduce the negative impact of label noise on the performance of transformers-based classification, we proposed two data augmentation approaches, Negative Embedding and Empathy for BERT and DistilBERT …
Cited by:4
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Seismic facies analysis: a deep domain adaptation approach
2022
M Quamer Nasim, Tannistha Maiti, Ayush Srivastava, Tarry Singh, Jie Mei
IEEE Transactions on Geoscience and Remote Sensing 60, 1-16
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Authors:M Quamer Nasim, Tannistha Maiti, Ayush Srivastava, Tarry Singh, Jie Mei
Publication date:02/16/2022
Journal:IEEE Transactions on Geoscience and Remote Sensing
Volume:60
Pages:1-16
Publisher:IEEE
Description:Deep neural networks (DNNs) can learn accurately from large quantities of labeled input data but often fail to do so when labeled data are scarce. DNNs sometimes fail to generalize on test data sampled from different input distributions. Unsupervised deep domain adaptation (DDA) techniques have been proven useful when no labels are available and when distribution shifts are observed in the target domain (TD). In this study, experiments are performed on seismic images of the F3 block 3-D dataset from offshore Netherlands [source domain (SD)] and Penobscot 3-D survey data from Canada (TD). Three geological classes from SD and TD that have similar reflection patterns are considered. A DNN architecture named EarthAdaptNet (EAN) is proposed to semantically segment the seismic images when few classes have data scarcity, and we use a transposed residual unit to replace the traditional dilated convolution in the …
Cited by:36
Resources:[PDF] from arxiv.org
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Informing deep neural networks by multiscale principles of neuromodulatory systems
2022
Jie Mei, Eilif Muller, Srikanth Ramaswamy
Trends in Neurosciences
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Authors:Jie Mei, Eilif Muller, Srikanth Ramaswamy
Publication date:01/21/2022
Journal:Trends in Neurosciences
Publisher:Elsevier Current Trends
Cited by:41
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Persisting chemosensory impairments in 366 healthcare workers following COVID-19: an 11-month follow-up
2022
Nicholas Bussiere, Jie Mei, Cindy Levesque-Boissonneault, Mathieu Blais, Sara Carazo, F…
Chemical Senses 47, bjac010
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Authors:Nicholas Bussiere, Jie Mei, Cindy Levesque-Boissonneault, Mathieu Blais, Sara Carazo, Francois Gros-Louis, Robert Laforce Jr, Gaston De Serres, Nicolas Dupre, Johannes Frasnelli
Publication date:01/01/2022
Journal:Chemical Senses
Volume:47
Pages:bjac010
Publisher:Oxford University Press
Description:Olfactory and gustatory dysfunctions (OD, GD) are prevalent symptoms following COVID-19 and persist in 6%–44% of individuals post-infection. As only few reports have described their prognosis after 6 months, our main objective was to assess the prevalence of OD and GD 11-month post-COVID-19. We also aimed to determine intraclass correlation coefficients (ICC) of chemosensory self-ratings for the follow-up of chemosensory sensitivity. We designed an observational study and distributed an online questionnaire assessing chemosensory function to healthcare workers with a RT-PCR-confirmed SARS-CoV-2 infection 5- and 11-month post-COVID-19. Specifically, we assessed olfaction, gustation, and trigeminal sensitivity (10-point visual analog scale) and function (4-point Likert scale). We further measured clinically relevant OD using the Chemosensory Perception Test, a psychophysical test designed to …
Cited by:15
Resources:[PDF] from medrxiv.org
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Identification of REM sleep behavior disorder by structural magnetic resonance imaging and machine learning
2021
Jie Mei, Shady Rahayel, Christian Desrosiers, Ronald B Postuma, Jacques Montplaisir, Ju…
medRxiv, 2021.09. 18.21263779
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Authors:Jie Mei, Shady Rahayel, Christian Desrosiers, Ronald B Postuma, Jacques Montplaisir, Julie Carrier, Oury Monchi, Johannes Frasnelli, Jean-François Gagnon
Publication date:09/22/2021
Journal:medRxiv
Pages:2021.09. 18.21263779
Publisher:Cold Spring Harbor Laboratory Press
Description:Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a major risk factor for synucleinopathies, and patients often present with clinical signs and morphological brain changes. However, there is an important heterogeneity in the presentation and progression of these alterations, and the brain regions that are more vulnerable to neurodegeneration remain to be determined. Objectives To assess the feasibility of morphology-based machine learning approaches in the identification and subtyping of iRBD. Methods For the three classification tasks [iRBD (n=48) vs controls (n=41); iRBD vs Parkinson’s disease (n=29); iRBD with mild cognitive impairment (n=16) vs without mild cognitive impairment (n=32)], machine learning models were trained with morphometric measurements (thickness, surface area, volume, and deformation) extracted from T1-weighted structural magnetic resonance imaging. Model performance and the most discriminative brain regions were analyzed and identified. Results A high accuracy was reported for iRBD vs controls (79.6%, deformation of the caudal middle frontal gyrus and putamen, thinning of the superior frontal gyrus, and reduced volume of the inferior parietal cortex and insula), iRBD vs Parkinson’s disease (82%, larger volume and surface area of the insula, thinning of the entorhinal cortex and lingual gyrus, and reduced volume of the fusiform gyrus), and iRBD with vs without mild cognitive impairment (84.8%, thinning of the pars triangularis, superior temporal gyrus, transverse temporal cortex, larger surface area of the superior temporal gyrus, and deformation of isthmus of the cingulate gyrus …
Cited by:5
Resources:[PDF] from medrxiv.org
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Machine learning for the diagnosis of Parkinson’s disease: a review of literature
2021
Jie Mei, Christian Desrosiers, Johannes Frasnelli
Frontiers in aging neuroscience 13, 633752
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Authors:Jie Mei, Christian Desrosiers, Johannes Frasnelli
Publication date:05/06/2021
Journal:Frontiers in aging neuroscience
Volume:13
Pages:633752
Publisher:Frontiers Media SA
Description:Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
Cited by:360
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Neural mechanisms of observational learning: a neural working model
2021
Weixi Kang, Sònia Pineda Hernández, Jie Mei
Frontiers in human neuroscience 14, 609312
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Authors:Weixi Kang, Sònia Pineda Hernández, Jie Mei
Publication date:04/22/2021
Journal:Frontiers in human neuroscience
Volume:14
Pages:609312
Publisher:Frontiers Media SA
Description:Humans and some animal species are able to learn stimulus-response (S-R) associations by observing others' behavior. It saves energy and time and avoids the danger of trying the wrong actions. Observational learning (OL) depends on the capability of mapping the actions of others into our own behaviors, processing outcomes, and combining this knowledge to serve our goals. Observational learning plays a central role in the learning of social skills, cultural knowledge, and tool use. Thus, it is one of the fundamental processes in which infants learn about and from adults (Byrne and Russon, ). In this paper, we review current methodological approaches employed in observational learning research. We highlight the important role of the prefrontal cortex and cognitive flexibility to support this learning process, develop a new neural working model of observational learning, illustrate how imitation relates to observational learning, and provide directions for future research.
Cited by:22
Resources:[HTML] from frontiersin.org
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Differentiation of Parkinson’s disease and non-Parkinsonian olfactory dysfunction with structural MRI data
2021
Jie Mei, Cécilia Tremblay, Nikola Stikov, Christian Desrosiers, Johannes Frasnelli
11597, 320-327
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Authors:Jie Mei, Cécilia Tremblay, Nikola Stikov, Christian Desrosiers, Johannes Frasnelli
Publication date:02/15/2021
Volume:11597
Pages:320-327
Publisher:SPIE
Cited by:8
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Chemosensory dysfunctions induced by COVID-19 can persist up to 7 months: a study of over 700 healthcare workers
2021
Nicholas Bussière, Jie Mei, Cindy Lévesque-Boissonneault, Mathieu Blais, Sara Carazo, F…
Chemical senses 46, bjab038
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Authors:Nicholas Bussière, Jie Mei, Cindy Lévesque-Boissonneault, Mathieu Blais, Sara Carazo, Francois Gros-Louis, Gaston De Serres, Nicolas Dupré, Johannes Frasnelli
Publication date:01/01/2021
Journal:Chemical senses
Volume:46
Pages:bjab038
Publisher:Oxford University Press
Description:Several studies have revealed either self-reported chemosensory alterations in large groups or objective quantified chemosensory impairments in smaller populations of patients diagnosed with COVID-19. However, due to the great variability in published results regarding COVID-19-induced chemosensory impairments and their follow-up, prognosis for chemosensory functions in patients with such complaints remains unclear. Our objective is to describe the various chemosensory alterations associated with COVID-19 and their prevalence and evolution after infection. A cross-sectional study of 704 healthcare workers with a RT–PCR-confirmed SARS-CoV-2 infection between 2020 February 28 and 2020 June 14 was conducted 3–7 months after onset of symptoms. Data were collected with an online questionnaire. Outcomes included differences in reported chemosensory self-assessment of olfactory …
Cited by:41
Resources:[PDF] from nih.gov
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Integrating GPU support for FreeSurfer with OpenACC
2020
Jingcheng Shen, Jie Mei, Marcus Walldén, Fumihiko Ino
, 1622-1628
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Authors:Jingcheng Shen, Jie Mei, Marcus Walldén, Fumihiko Ino
Publication date:12/11/2020
Pages:1622-1628
Publisher:IEEE
Description:FreeSurfer is among the most widely used suites of software for the study of cortical and subcortical brain anatomy. However, analysis using FreeSurfer can be time-consuming and it lacks support for the graphics processing units (GPUs) after the core development team stopped maintaining GPU-accelerated versions due to significant programming cost. As FreeSurfer is a large project with millions of source lines, in this work, we introduce and examine the use of a directive-based framework, OpenACC, in GPU acceleration of FreeSurfer, and we found the OpenACC-based approach significantly reduces programming costs. Moreover, because the overhead incurred by CPU-to-GPU data transfer is the major challenge in delivering GPU-based codes of high performance, we compare two schemes, copy-and-transfer and overlapped-full-transfer, to reduce such data transfer overhead. Experimental results show that …
Cited by:2
Resources:[PDF] from biorxiv.org
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Automated radial 8-arm maze: A voluntary and stress-free behavior test to assess spatial learning and memory in mice
2020
Jie Mei, Joel Kohler, York Winter, Claudia Spies, Matthias Endres, Stefanie Banneke, Ju…
Behavioural Brain Research 381, 112352
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Authors:Jie Mei, Joel Kohler, York Winter, Claudia Spies, Matthias Endres, Stefanie Banneke, Julius Valentin Emmrich
Publication date:03/02/2020
Journal:Behavioural Brain Research
Volume:381
Pages:112352
Publisher:Elsevier
Description:The radial arm maze (RAM) is a common behavioral test to assess spatial working and reference memory in mice. However, conventional RAM experiments require a substantial degree of manual handling and animals are usually subjected to prolonged periods of food or water deprivation to achieve sufficient learning motivation resulting in stress-induced confounding effects and unwanted intra- and inter-subject variation.In a proof-of-concept approach to improve reliability and repeatability of results by refining the conventional maze methodology, we developed a voluntary, fully automated 8-arm RAM and tested its feasibility and usability using both spatial working and combined working/reference memory paradigms in ten female C57BL/6J mice.We demonstrate that experimental procedures of up to 7 days duration could be conducted without any manual animal handling and that mice up to 18 months of age …
Cited by:41
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An energy efficient time-mode digit classification neural network implementation
2020
OC Akgun, J Mei
Philosophical Transactions of the Royal Society A 378 (2164), 20190163
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Authors:OC Akgun, J Mei
Publication date:02/07/2020
Journal:Philosophical Transactions of the Royal Society A
Volume:378
Issue:2164
Pages:20190163
Publisher:The Royal Society Publishing
Description:This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme …
Cited by:8
Resources:[PDF] from royalsocietypublishing.org
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Olfactory bulb surroundings can help to distinguish Parkinson’s disease from non-parkinsonian olfactory dysfunction
2020
Cécilia Tremblay, Jie Mei, Johannes Frasnelli
NeuroImage: Clinical 28, 102457
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Authors:Cécilia Tremblay, Jie Mei, Johannes Frasnelli
Publication date:01/01/2020
Journal:NeuroImage: Clinical
Volume:28
Pages:102457
Publisher:Elsevier
Description:Background Methods Results
Cited by:29
Resources:[HTML] from sciencedirect.com
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Intra-thalamic and thalamocortical connectivity: potential implication for deep learning
2018
Jie Mei, Tarry Singh
Proceedings of the 1st International Workshop on Software Engineering for Cognitive Ser…
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Authors:Jie Mei, Tarry Singh
Publication date:05/28/2018
Journal:Proceedings of the 1st International Workshop on Software Engineering for Cognitive Services
Pages:9-14
Description:Contrary to the traditional view that the thalamus acts as a passive relay station of sensory information to the cortex, a number of experimental studies have demonstrated the effects of peri-geniculate and cortico-thalamic projections on the transmission of visual input. In the present study, we implemented a mechanistic model to facilitate the understanding of peri-geniculate and cortico-thalamic effects on the transfer function of geniculate cells and their firing patterns. As a result, the model successfully captures some fundamental properties of early-stage visual processing in mammalian brain. We conclude, therefore, that the thalamus is not a passive relay center and the intra-thalamic circuitry is of great importance to biological vision. In summary, intra-thalamic and thalamocortical circuitries have implications in early-stage visual processing, and could constitute a valid tool for refining information relay and …
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Body temperature measurement in mice during acute illness: implantable temperature transponder versus surface infrared thermometry
2018
Jie Mei, Nico Riedel, Ulrike Grittner, Matthias Endres, Stefanie Banneke, Julius Valent…
Scientific reports 8 (1), 1-10
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Authors:Jie Mei, Nico Riedel, Ulrike Grittner, Matthias Endres, Stefanie Banneke, Julius Valentin Emmrich
Publication date:02/23/2018
Journal:Scientific reports
Volume:8
Issue:1
Pages:1-10
Publisher:Nature Publishing Group
Description:Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra-and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an …
Cited by:117
Resources:[HTML] from nature.com
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Systems of Global Governance in the Era of Human-Machine Convergence
2018
Eugenio Maria Battaglia, Jie Mei, Guillaume Dumas
arXiv preprint arXiv:1802.04255
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Authors:Eugenio Maria Battaglia, Jie Mei, Guillaume Dumas
Publication date:02/11/2018
Journal:arXiv preprint arXiv:1802.04255
Description:Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest …
Cited by:2
Resources:[PDF] from arxiv.org
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A multimenu system based on the p300 component as a time saving procedure for communication with a brain-computer interface
2013
Joanna Jarmolowska, Marcello M Turconi, Pierpaolo Busan, Jie Mei, Piero P Battaglini
Frontiers in neuroscience 7, 39
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Authors:Joanna Jarmolowska, Marcello M Turconi, Pierpaolo Busan, Jie Mei, Piero P Battaglini
Publication date:03/25/2013
Journal:Frontiers in neuroscience
Volume:7
Pages:39
Publisher:Frontiers Media SA
Description:The present study investigates a Brain-Computer Interface (BCI) spelling procedure based on the P300 evoked potential. It uses a small matrix of words arranged in a tree-shaped organization (“multimenu”), and allows the user to build phrases one word at a time, instead of letter by letter. Experiments were performed in two sessions on a group of seven healthy volunteers. In the former, the “multimenu” was tested with a total of 60 choices: 30 “externally-imposed” selections and 30 “free-choice” selections. In the latter, 3 × 3 matrices were compared with 6 × 6 matrices. Each matrix was composed of letters or words, for a total of four matrices. Differences in classifier accuracy, bit rate and amplitude of the evoked P300 were evaluated. Average accuracy in all subjects was 87% with no differences between the selection methods. The 3 × 3 “multimenu” obtained the same level of classifier accuracy as the 6 × 6 matrices, even with a significantly lower amplitude of the P300. Bit rate was increased when using the 3 × 3 matrices compared to the 6 × 6 ones. The “multimenu” system was equally effective, but faster than conventional, letter-based matrices. By improving the speed of communication, this method can be of help to patients with severe difficulties in communication.
Cited by:9
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