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Journals in Neuroscience

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Neuroscience: Top Neuroscience Journals

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Top 20 Journals by Impact Factor:

• Nature Reviews Neuroscience
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• Journal of Pineal Research
• Trends in Neurosciences
• Annual Review of Neuroscience
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• Progress in Neurobiology
• Nature Human Behaviour
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October 2024 - Volume 56 - Issue 5 : Journal of Neuroscience Nursing

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Do You Believe in Speed Limits?

Journal of Neuroscience Nursing. 56(5):143, October 2024.

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Letter to the Editor: Digital Badging Is Dissemination Beyond the Edge of the Page

Journal of Neuroscience Nursing. 56(5):144, October 2024.

Letter to the Editor: Evaluating RÁPIDO as a Spanish Stroke Awareness Mnemonic

Journal of Neuroscience Nursing. 56(5):145, October 2024.

Impact of Illness Uncertainty on Health-Related Quality of Life in Patients With Unruptured Intracranial Aneurysms After Coil Embolization

Journal of Neuroscience Nursing. 56(5):146-151, October 2024.

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Assessing Delirium in Patients With Neurological Diseases

Journal of Neuroscience Nursing. 56(5):157-163, October 2024.

Anxiety Symptoms and Disease Severity in Parkinson Disease

Journal of Neuroscience Nursing. 56(5):169-173, October 2024.

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Implementing Advance Care Planning and Care Coordination in the Care for People With Parkinson Disease: A Feasibility Study

Journal of Neuroscience Nursing. 56(5):174-179, October 2024.

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Improving Safety and Satisfaction in the Safety Monitoring Unit

Journal of Neuroscience Nursing. 56(5):152-156, October 2024.

Management of Idiopathic Intracranial Hypertension: A Case Report

Journal of Neuroscience Nursing. 56(5):164-168, October 2024.

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A Multidisciplinary Approach to Increase Dysphagia Compliance in Stroke Patients

Journal of Neuroscience Nursing. 56(5):180-185, October 2024.

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Neuroleadership: A Concept Analysis and Implications for Nursing

Journal of Neuroscience Nursing. 56(5):186-191, October 2024.

Assessing Delirium in Patients with Neurological Diseases

Journal of Neuroscience Nursing. 56(5):E5, October 2024.

New blood test detects ALS with 98% accuracy, offering hope for earlier diagnosis

A simple blood test has been shown to be 98% accurate at distinguishing between patients with ALS and those without the disease, with potential implications for treatment if it's approved for clinical use.

A simple blood test could speed the diagnosis of the deadly nerve disease amyotrophic lateral sclerosis (ALS), new research suggests. If the test gets regulatory approval, it could help patients start treatment that slows the disease's progression earlier than they would with conventional diagnostics, its developers say.

The new test works by detecting eight small molecules known as microRNAs , which help regulate which genes are turned on and how active they are. These eight molecules are found within tiny packages that are released into the blood from cells of the nervous system in patients with ALS. They act like a "fingerprint" of the disease that can then be detected in the blood.

In a new study, published Thursday (Sept. 12) in the journal Brain Communications , the test was 98% accurate at distinguishing between blood samples taken from 119 people with diagnosed ALS and samples from 150 people without the disease. However, it's not yet known whether the test can accurately differentiate people with ALS from those with other neurological diseases, such as Parkinson's , so more tests will be needed.

The researchers who developed the test say that, with further evaluation, it could become a useful tool for diagnosing ALS. There is currently no cure for the disease, but earlier diagnosis could help hasten patients' access to treatments that help slow physical decline . Such treatments include drugs such as riluzole and edaravone.

Related: Some people recover from ALS — now, we might know why

ALS is a neurodegenerative disorder that affects the neurons in the brain and spinal cord that control the voluntary movement of muscles, including those required for breathing . Initially, patients may experience symptoms such as muscle twitching and cramping. Over time, the disease progresses, causing patients to struggle with everyday tasks, such as eating, speaking and, eventually, breathing.

Most patients with ALS die of respiratory failure within three to five years of their symptoms first appearing.

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The disease is currently diagnosed through an extensive clinical examination conducted by a neurologist, Sandra Banack , lead study author and a scientist at Brain Chemistry Labs, a nonprofit research institute in Wyoming, told Live Science in an email. However, the symptoms of ALS often mimic those of other neurological diseases, such as multiple sclerosis and Parkinson's disease , which also cause nerve damage. This is especially true in the earlier stages of these diseases .

So, to confidently diagnose ALS, neurologists must monitor how patients' symptoms progress over time, Banack said. But because patients' survival time after their symptoms begin is short, many patients "deteriorate significantly" before they can secure a diagnosis, she said.

Thus, a blood test that could help reveal ALS sooner could be a "game-changer, ultimately improving time to diagnosis, reducing patient and family anxiety, lowering diagnostic costs, and supporting the development of new drugs," Banack said.

On that last note, the tests could help flag specific biological pathways that could be targeted by new drugs. For instance, the eight microRNAs are known to be involved in processes such as neuroinflammation and programmed cell death, or apoptosis, which play a role in ALS.

As part of their new study, the researchers also investigated whether the blood test could accurately distinguish between patients with ALS and those with either Parkinson's disease or a very similar condition to ALS known as primary lateral sclerosis (PLS). However, their sample sizes of patients with Parkinson's and PLS were too small to make conclusions about the tests' accuracy at this stage, Banack noted.

Ideally, the blood test would be able to reliably spot cases of ALS and also identify people who definitely do not have the disease. You wouldn't want the test to erroneously flag a case of Parkinson's as ALS, for instance. Thus, the researchers intend to continue assessing the accuracy of the test with more blood samples from people with ALS and people with different diseases.

They hope to bring the test to market as soon as possible.

— 11 children diagnosed with new form of ALS

— Intense exercise could trigger ALS in those with genetic risk

— Brain-computer interface helps patient with locked-in syndrome communicate

"We are actively searching for the right diagnostic company partner who can rapidly make this test available to patients and clinicians," Banack said. "In the best case scenario, it could be available in about 18 months."

The team envisions that clinicians could use the test to quickly confirm or dispel any doubts they may have about a patient's ALS diagnosis after initial examinations.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun ? Send us your questions about how the human body works to [email protected] with the subject line "Health Desk Q," and you may see your question answered on the website!

Emily is a health news writer based in London, United Kingdom. She holds a bachelor's degree in biology from Durham University and a master's degree in clinical and therapeutic neuroscience from Oxford University. She has worked in science communication, medical writing and as a local news reporter while undertaking journalism training. In 2018, she was named one of MHP Communications' 30 journalists to watch under 30. ( [email protected] ) 

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Metformin May Play a Surprising Role in Radiation Protection

Improving radioprotection for at-risk populations, such as cancer patients and astronauts, is crucial.

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High doses of radiation pose significant risks to the human body, particularly for patients with cancer undergoing treatment or astronauts exposed to intense radiation from the Sun. For this reason, improving radioprotection for these populations is crucial. A study, led by Dr. Silvia Siteni at the University of Texas Southwestern Medical Center and published in PLOS One , demonstrates the role that metformin could play in radioprotection for these at-risk groups.

The potential of metformin

Metformin is a medication currently used for treating diabetes mellitus type 2 and has been prescribed to over 150 million patients worldwide, proving particularly useful because of its ability to reduce hyperglycemia without causing hypoglycemia or weight gain. Although its mechanism of action is still not well understood, it is known to have antioxidant effects through mitochondrial targeting and activation of adenosine monophosphate-activated protein kinase (AMPK). Recent research suggests metformin may also have potential as a radioprotector, as evidenced by its ability to protect human cells from radiation-induced DNA damage and oxidative stress.

In vitro and in vivo mechanisms of metformin’s activity

The researchers first performed a CellTiter-Glo (CTG) analysis to understand the short-term effects of metformin on human BJ fibroblasts. From this, they conducted a clonogenic assay to evaluate the long-term survival of these cells after treatment with various metformin concentrations. Next, they wanted to investigate the activation of AMPK and the role of superoxide dismutase (SOD1) in the radioprotective pathway, so they treated cells with 0.5 mM metformin and analyzed protein expression via western blot. They also irradiated the BJ fibroblasts with gamma rays after metformin treatment and assessed DNA damage via Comet assays and γH2AX foci. In vivo studies were conducted where they treated mice with metformin and then exposed them to ionizing radiation or simulated galactic cosmic radiation, and then assessed DNA damage and survival afterwards.

The key findings from this study were that:

• 0.5 mM metformin significantly increased the expression of SOD1 and phosphorylated AMPK in BJ fibroblasts
• Higher doses of metformin reduced cell survival
• Treatment with metformin significantly reduced DNA damage in irradiated BJ fibroblasts
• Metformin treatment reduced the number of micronuclei in bone marrow cells of the mice and decreased DNA damage in their colon and lung tissues
• Metformin improved survival of mice who were exposed to ionizing radiation, specifically with 37% of treated mice surviving compared to 0% of untreated mice

Metformin as a pre-treatment for radiation exposure

Metformin was able to increase the expression of SOD1 and phosphorylated AMPK, both of which indicate the activation of detoxification pathways, which is desirable in people who are exposed to high levels of radiation. Furthermore, treatment with metformin reduced DNA damage in fibroblasts, also indicating its protective effect against radiation. The in vivo studies are also promising, in that metformin reduced the number of micronuclei in bone marrow cells, which are indicators of DNA damage or genomic instability. Additionally, the treatment improved overall survival in mice, suggesting overarching improvement in health.

High doses of radiation are capable of causing significant DNA damage and the results of this study led by Dr. Siteni show that metformin has promise as a pre-treatment for people exposed to intense concentrations of radiation, such as cancer patients and astronauts. However, further experimentation is required before this research is considered for clinical trials. Firstly, dose optimization is crucial, as high concentrations of metformin reduced overall cell survival in this study. In order for metformin to be safe to administer for radioprotection, the current dosage that maximizes efficacy and safety is important. Furthermore, although AMPK and SOD1 activation were linked to metformin’s protective effects, the specific molecular mechanisms behind DNA repair and reduced oxidative stress still needs further understanding. Thinking into the future, if metformin were to be administered to humans on a regular basis for radioprotection over the course of cancer treatment, it’s crucial to also conduct long-term studies in vivo to understand its impact over a longer period of time.

Next steps before clinical trials

This study shows promise in using metformin for radioprotection. This could be particularly useful for patients with cancer who receive frequent doses of radiation or astronauts who spend a lot of time exposed to dangerous levels of radiation through explosions on the solar surface and galactic cosmic radiation. However, before stepping into clinical trials, scientists must determine the most effective dose for radioprotection and better understand the long-term impacts of the drug when used at that level.

Reference:  Siteni S, Barron S, Luitel K, Shay JW. Radioprotective effect of the anti-diabetic drug metformin. PLoS One . 2024;19(7):e0307598. doi: 10.1371/journal.pone.0307598

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The Big City Is Vibrant. Birds There Might Be Getting Less So.

Recent studies show that certain feather pigments can help neutralize toxic pollution. It means darker, duller birds could have a survival advantage.

By Marta Zaraska

Some popular city dwellers appear to be losing their colorful allure, and not just the dirty birds.

According to a study published this summer in the journal Landscape and Planning that looked at 547 bird species in China, birds that live in cities are duller and darker on average than their rural counterparts. A similar conclusion emerged from an analysis of 59 studies published in March in Biological Reviews : Urban feathers are not as bright, with yellow, orange and red feathers affected most.

Often, city birds are covered in grime. But even if you could give them all a good bird bath, chances are their brightness still wouldn’t match that of their country cousins. That’s because of the way pollution, and heavy metals in particular, can interact with melanin, a pigment that makes feathers black, brown and gray.

Studies show that melanin can bind to heavy metals like lead. That means toxic chemicals may be more likely to be stored in plumage in darker and duller birds. And that, in turn, can confer a survival advantage.

“The more melanin you accumulate, the better able you are to sequester these harmful compounds in feathers,” said Kevin McGraw, a biologist at Michigan State University who studies the colors of animals to understand the costs, benefits and evolution of visual signals.

Urban pollution affects avian colors in other ways, too. Research shows that, compared with rural plants, city trees store fewer natural pigments called carotenoids. And pollution is the likely reason. Carotenoids are produced by plants, algae and fungi. They’re what makes red peppers red and carrots orange.

When leaves are low on these pigments, the effects go up the food chain: Leaf-munching caterpillars become deficient in carotenoids, and so do caterpillar-munching birds.

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Distinct neuron types contribute to hybrid auditory spatial coding

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Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons’ preference for ILD off the midline and the excitatory neurons’ heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.

Significance Statement Over the decades, studies have proposed three sound source decoders: the space map decoder (topographically tuned to sound location), the opponent channel decoder (compares the averaged tuning between two groups of neurons), and the population pattern decoder (decodes locations by utilizing the diverse tunings across the population). This is the first study that 1) visualizes the local organization of spatial tuning and identifies clusters in a brain area that features an auditory spatial map, 2) tests the three decoders in a single brain area of the same species and discovers that distinct neuron types favor different decoders, and 3) reveals the differential spatial coding between excitatory and inhibitory neurons and elucidates this disparity through a computational model.

The authors declare that they have no conflict of interest.

We appreciate Professor Bo Hong, and the members of his laboratory, Yili Yan and Li Shen, for discussing, sound stimuli generation, and speaker calibration. We appreciate Professor Mitchell Day and Nicholas Lesica for sharing us the codes for neural decoding, Professor Xiaowei Chen and Kexin Yuan for technical assistance, and two anonymous reviewers for their comprehensive, constructive, and inspiring comments. Funding: National Natural Science Foundation of China grant 61836004 (SS) Institute Guo Qiang (SS) Beijing Brain Science Special Project grant No. Z181100001518006 (SS) Tsinghua University Initiative Scientific Research Program 20197010009 (SS) IDG/McGovern Institute for Brain Research at Tsinghua University (SS) National Key Research and Development Program of China 2021ZD0200300 (SS).

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Collection  10 March 2022

Top 100 in Neuroscience

This collection highlights our most downloaded* neuroscience papers published in 2021. Featuring authors from around the world, these papers showcase valuable research from an international community.

*Data obtained from SN Inights, which is based on Digital Science's Dimensions.

Musical components important for the Mozart K448 effect in epilepsy

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Splitting sleep between the night and a daytime nap reduces homeostatic sleep pressure and enhances long-term memory

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Listening to speech with a guinea pig-to-human brain-to-brain interface

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Benefit of human moderate running boosting mood and executive function coinciding with bilateral prefrontal activation

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Enhanced activations in syntax-related regions for multilinguals while acquiring a new language

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Longitudinal effects of meditation on brain resting-state functional connectivity

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Evidence of a new hidden neural network into deep fasciae

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A directional 3D neurite outgrowth model for studying motor axon biology and disease

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The role of dorsolateral and ventromedial prefrontal cortex in the processing of emotional dimensions

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How neurons exploit fractal geometry to optimize their network connectivity

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Defining early changes in Alzheimer’s disease from RNA sequencing of brain regions differentially affected by pathology

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Narcissistic personality traits and prefrontal brain structure

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Sex-dependent alterations in behavior, drug responses and dopamine transporter expression in heterozygous DAT-Cre mice

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Functional network connectivity during Jazz improvisation

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Buspirone alleviates anxiety, depression, and colitis; and modulates gut microbiota in mice

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Human cerebral organoids as a therapeutic drug screening model for Creutzfeldt–Jakob disease

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Thioflavin-positive tau aggregates complicating quantification of amyloid plaques in the brain of 5XFAD transgenic mouse model

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Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

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Post-traumatic seizures and antiepileptic therapy as predictors of the functional outcome in patients with traumatic brain injury

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Human α-synuclein overexpression in a mouse model of Parkinson’s disease leads to vascular pathology, blood brain barrier leakage and pericyte activation

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Comparative neuroanatomy of the lumbosacral spinal cord of the rat, cat, pig, monkey, and human

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Deep learning-Based 3D inpainting of brain MR images

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Both high fat and high carbohydrate diets impair vagus nerve signaling of satiety

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PINK1 deficiency impairs adult neurogenesis of dopaminergic neurons

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Altered spontaneous activity in the frontal gyrus in dry eye: a resting-state functional MRI study

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Cell type-specific changes in transcriptomic profiles of endothelial cells, iPSC-derived neurons and astrocytes cultured on microfluidic chips

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Brain MRI in SARS-CoV-2 pneumonia patients with newly developed neurological manifestations suggestive of brain involvement

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Diazepam causes sedative rather than anxiolytic effects in C57BL/6J mice

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Photons detected in the active nerve by photographic technique

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Cannabidiol induces autophagy via ERK1/2 activation in neural cells

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Deep learning-based pupil model predicts time and spectral dependent light responses

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ATP signaling in the integrative neural center of Aplysia californica

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EEG signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

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Characterization of mitochondrial health from human peripheral blood mononuclear cells to cerebral organoids derived from induced pluripotent stem cells

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Astrocytic expression of the Alzheimer’s disease risk allele, ApoEε4, potentiates neuronal tau pathology in multiple preclinical models

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Effects of the sigma-1 receptor agonist blarcamesine in a murine model of fragile X syndrome: neurobehavioral phenotypes and receptor occupancy

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Magnetic domains oscillation in the brain with neurodegenerative disease

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Right frontal anxiolytic-sensitive EEG ‘theta’ rhythm in the stop-signal task is a theory-based anxiety disorder biomarker

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Role of miRNAs shuttled by mesenchymal stem cell-derived small extracellular vesicles in modulating neuroinflammation

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Position sensitive measurement of trace lithium in the brain with NIK (neutron-induced coincidence method) in suicide

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• R. Gernhäuser

Major oscillations in spontaneous home-cage activity in C57BL/6 mice housed under constant conditions

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Involvement of the dopaminergic system in the reward-related behavior of pregabalin

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A high-density electroencephalography study reveals abnormal sleep homeostasis in patients with rapid eye movement sleep behavior disorder

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MEG signatures of long-term effects of agreement and disagreement with the majority

• V. Klucharev
• A. Shestakova

Stroke prediction in patients presenting with isolated dizziness in the emergency department

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• Hong Jun Bae
• Won Young Kim

Age-dependent and region-specific alteration of parvalbumin neurons, perineuronal nets and microglia in the mouse prefrontal cortex and hippocampus following obesogenic diet consumption

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• Claire A. Lemieux
• Lisa M. Saksida

Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings

• Richárd Fiáth
• Domokos Meszéna
• István Ulbert

Amplification of potential thermogenetic mechanisms in cetacean brains compared to artiodactyl brains

• Paul R. Manger
• Nina Patzke

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

• Marion I. van den Heuvel
• Jasmine L. Hect
• Moriah E. Thomason

An anomaly detection approach to identify chronic brain infarcts on MRI

• Kees M. van Hespen
• Jaco J. M. Zwanenburg
• Hugo J. Kuijf

The neurodynamic treatment induces biological changes in sensory and motor neurons in vitro

• Giacomo Carta
• Giovanna Gambarotta
• Federica Fregnan

Optimal flickering light stimulation for entraining gamma waves in the human brain

• Kanghee Lee
• Yeseung Park
• Ki Woong Kim

Two-photon GCaMP6f imaging of infrared neural stimulation evoked calcium signals in mouse cortical neurons in vivo

• Attila Kaszas
• Gergely Szalay
• David Moreau

The Aβ(1–38) peptide is a negative regulator of the Aβ(1–42) peptide implicated in Alzheimer disease progression

• Maa O. Quartey
• Jennifer N. K. Nyarko
• Darrell D. Mousseau

Drinking coffee enhances neurocognitive function by reorganizing brain functional connectivity

• Sung Hoon Kang
• Jung Bin Kim

The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

• Matteo Marucci
• Gianluca Di Flumeri
• Pietro Aricò

CuATSM improves motor function and extends survival but is not tolerated at a high dose in SOD1 G93A mice with a C57BL/6 background

• Jeremy S. Lum
• Mikayla L. Brown
• Justin J. Yerbury

Underlying neurological mechanisms associated with symptomatic convergence insufficiency

• Tara L. Alvarez
• Mitchell Scheiman
• Bharat B. Biswal

Paternal exposure to a common pharmaceutical (Ritalin) has transgenerational effects on the behaviour of Trinidadian guppies

• Alex R. De Serrano
• Kimberly A. Hughes
• F. Helen Rodd

Behavioral arrest and a characteristic slow waveform are hallmark responses to selective 5-HT 2A receptor activation

• April Contreras
• Matthew Khumnark
• Dustin J. Hines

Focused ultrasound mediated blood–brain barrier opening is safe and feasible in a murine pontine glioma model

• Zachary K. Englander
• Hong-Jian Wei
• Cheng-Chia Wu

Basmisanil, a highly selective GABA A -α5 negative allosteric modulator: preclinical pharmacology and demonstration of functional target engagement in man

• Joerg F. Hipp
• Frederic Knoflach
• Maria-Clemencia Hernandez

Spatial memory deficiency early in 6xTg Alzheimer’s disease mouse model

• Shinwoo Kang
• Keun-A Chang

The gut microbiome is associated with brain structure and function in schizophrenia

Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

• Abhishek Uday Patil
• Sejal Ghate
• Chih-Mao Huang

Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit

• Anil K. Palepu
• Aditya Murali
• Robert D. Stevens

LSD-stimulated behaviors in mice require β-arrestin 2 but not β-arrestin 1

• Ramona M. Rodriguiz
• Vineet Nadkarni
• William C. Wetsel

Modified wavelet analysis of ECoG-pattern as promising tool for detection of the blood–brain barrier leakage

• Anastasiya Runnova
• Maksim Zhuravlev
• Jurgen Kurths

Adrenergic inhibition facilitates normalization of extracellular potassium after cortical spreading depolarization

• Hiromu Monai
• Shinnosuke Koketsu
• Hajime Hirase

An atlas for human brain myelin content throughout the adult life span

• Adam V. Dvorak
• Taylor Swift-LaPointe
• Shannon H. Kolind

Immediate and after effects of transcranial direct-current stimulation in the mouse primary somatosensory cortex

• Carlos A. Sánchez-León
• Isabel Cordones
• Javier Márquez-Ruiz

Human brain dynamics in active spatial navigation

• Tien-Thong Nguyen Do
• Chin-Teng Lin
• Klaus Gramann

Reading without phonology: ERP evidence from skilled deaf readers of Spanish

• Brendan Costello
• Sendy Caffarra
• Manuel Carreiras

Alterations in reward network functional connectivity are associated with increased food addiction in obese individuals

• Soumya Ravichandran
• Ravi R. Bhatt
• Arpana Gupta

Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia

• Soo Hyun Cho
• Sookyoung Woo
• Sang Won Seo

The structure dilemma in biological and artificial neural networks

• Thomas Pircher
• Bianca Pircher
• Andreas Feigenspan

Voluntary intake of psychoactive substances is regulated by the dopamine receptor Dop1R1 in Drosophila

• Shun Hiramatsu
• Toshiharu Ichinose

The contribution of platelets to peripheral BDNF elevation in children with autism spectrum disorder

• Cristan A. Farmer
• Audrey E. Thurm
• Joan C. Han

Dopamine-loaded nanoparticle systems circumvent the blood–brain barrier restoring motor function in mouse model for Parkinson’s Disease

• Victoria Monge-Fuentes
• Andréia Biolchi Mayer
• Márcia Renata Mortari

NK1 antagonists attenuate tau phosphorylation after blast and repeated concussive injury

• Frances Corrigan
• Ibolja Cernak
• Robert Vink

Nicotine suppresses Parkinson’s disease like phenotypes induced by Synphilin-1 overexpression in Drosophila melanogaster by increasing tyrosine hydroxylase and dopamine levels

• Angel Carvajal-Oliveros
• Carmen Domínguez-Baleón
• Enrique Reynaud

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer’s disease

• Maurizio Bergamino
• Ryan R. Walsh
• Ashley M. Stokes

A functional spiking neuronal network for tactile sensing pathway to process edge orientation

• Adel Parvizi-Fard
• Mahmood Amiri
• Nitish V. Thakor

The difficulty to model Huntington’s disease in vitro using striatal medium spiny neurons differentiated from human induced pluripotent stem cells

• Kim Le Cann
• Alec Foerster
• Angelika Lampert

Hippocampus-retrosplenial cortex interaction is increased during phasic REM and contributes to memory consolidation

• Daniel Gomes de Almeida-Filho
• Bruna Del Vechio Koike
• Claudio Marcos Queiroz

Machine learning-based classification of mitochondrial morphology in primary neurons and brain

• Garrett M. Fogo
• Anthony R. Anzell
• Thomas H. Sanderson

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Research Forum Brief | June 2024

MatterGen: A Generative Model for Materials Design

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Presented by  Tian Xie at  Microsoft Research Forum, June 2024

“Materials design is the cornerstone of modern technology. Many of the challenges our society is facing today are bottlenecked by finding a good material. … If we can find a novel material that conducts lithium very well, it will be a key component for our next-generation battery technology. The same applies to many other domains.” – Tian Xie, Principal Research Manager, Microsoft Research AI for Science

Transcript: Lightning Talk

Tian Xie , Principal Research Manager, Microsoft Research AI for Science

Tian Xie introduces MatterGen , a generative model that creates new inorganic materials based on a broad range of property conditions required by the application, aiming to shift the traditional paradigm of materials design with generative AI.

Microsoft Research Forum, June 4, 2024

TIAN XIE: Hello, everyone. My name is Tian, and I’m from Microsoft Research AI for Science. I’m excited to be here to share with you MatterGen, our latest model that brings generative AI to materials design.

Materials design is the cornerstone of modern technology. Many of the challenges our society is facing today are bottlenecked by finding a good material. For example, if we can find a novel material that conducts lithium very well, it will be a key component for our next-generation battery technology. The same applies to many other domains, like finding a novel material for solar cells, carbon capture, and quantum computers. Traditionally, materials design is conducted by search-based methods. We search through a list of candidates and gradually filter them using a list of design criteria for the application. Like for batteries, we need the materials to contain lithium, to be stable, to have a high lithium-ion conductivity, and each filtering step can be conducted using simulation-based methods or AI emulators. At the end, we get five to 10 candidates that we’re sending to the lab for experimental synthesis.

In MatterGen, we hope to rethink this process with generative AI. We’re aiming to directly generate materials given the design requirements for the target application, bypassing the process of searching through candidates. You can think of it as using text-to-image generative models like DALL-E to generate the images given a prompt rather than needing to search through the entire internet for images via a search engine. The core of MatterGen is a diffusion model specifically designed for materials. A material can be represented by its unit cell, the smallest repeating unit of the infinite periodic structure. It has three components: atom types, atom positions, and periodic lattice. We designed the forward process to corrupt all three components towards a random structure and then have a model to reverse this process to generate a novel material. Conceptually, it is similar to using a diffusion model for images, but we build a lot of inductive bias like equivariance and periodicity into the model because we’re operating on a sparse data region as in most scientific domains.

Given this diffusion architecture, we train the base model of MatterGen using the structure of all known stable materials. Once trained, we can generate novel, stable materials by sampling from the base model unconditionally. To generate the material given desired conditions, we further fine-tune this base model by adding conditions to each layer of the network using a ControlNet-style parameter-efficient fine-tuning approach. The condition can be anything like a specific chemistry, symmetry, or any target property. Once fine-tuned, the model can directly generate the materials given desired conditions. Since we use fine-tuning, we only need a small labeled dataset to generate the materials given the corresponding condition, which is actually very useful for the users because it’s usually computationally expensive to generate a property-labeled dataset for materials.

Here’s an example of how MatterGen generates novel materials in the strontium-vanadium- oxygen chemical system. It generates candidates with lower energy than two other competing methods: random structure search and substitution. The resulting structure looks very reasonable and is proven to be stable using computational methods. MatterGen also generates materials given desired magnetic, electronic, and mechanical properties. The most impressive result here is that we can shift the distribution of generated material towards extreme values compared with training property. This is very significant because most of the materials design problem involves finding materials with extreme properties, like finding superhard materials, magnets with high magnetism, which is difficult to do with traditional search-based methods and is the key advantage of generative models.

Our major next step is to bring this generative AI–designed materials into the real life, making real-world impact in a variety of domains like battery design, solar cell design, and carbon capture. One limitation is that we only have validated this AI-generated materials using computation. We’re working with experimental partners to synthesize them in the wet lab. It is a nontrivial process, but we keep improving our model, getting feedbacks from the experimentalist, and we are looking forward to a future where generative AI–designed materials can make real-world impact in a broad range of domains. Here’s a link to our paper in case you want to learn more about the details. We look forward to any comments and feedbacks that you might have. Thank you very much.

MatterGen: Designing materials with generative AI 

By Tian Xie

MatterGen, a model developed by Microsoft Research AI for Science, applies generative AI to materials design.

Why is this important?

Materials design is the cornerstone of modern technology. Many of the challenges our society is facing today are bottlenecked by scientists’ inability to find good materials that can unlock solutions. If we can find a novel material that conducts lithium ion extremely well, for example, it will be a key component of next-generation battery technology. The same applies to many other domains, like finding novel materials for solar cells, carbon capture, and quantum computers. 

Traditionally, materials design is conducted by search-based methods. We search through a list of candidates and gradually filter them down with a list of design requirements for the application. With batteries, for example, we need the material to contain lithium, to be stable, to have high lithium-ion conductivity, and so on. Each filtering step can be conducted using quantum mechanical simulations or AI emulators. Finally, we end up with 5-10 candidates that can be sent to the lab for experimental synthesis.

In MatterGen , we hope to rethink this process using generative AI. We aim to directly generate materials, given the design requirements for the target application, bypassing the tedious process of searching through a large number of candidates. You can think of it as using text-image generative models like DALLE to generate images given a detailed prompt, rather than using a search engine to scour the entire Internet for specific images.

The core of MatterGen is a diffusion model specifically designed for materials. A material can be represented by its unit cell, the smallest repeating unit of the infinite periodic structure. It has three components: atom types; atom positions; and the periodic lattice. We design the forward process to corrupt all three components toward a random material, and then train a model to reverse the corruption process to generate novel materials. Conceptually, it is similar to a diffusion model for images, but we build a lot of inductive bias, like equivariance and periodicity, into the model because we operate on the sparse data region as in most scientific domains.

Given this diffusion architecture, we train the base model of MatterGen using the structure of all known stable materials. Once the model is trained, we can generate novel, stable materials by sampling from the base model unconditionally. 

To generate materials given the desired conditions, we further fine-tune this base model by adding conditions to each layer of the network, using a ControlNet-style parameter efficient fine-tuning approach. The conditions can be anything, like a specific chemistry, symmetry, or any target property. Once fine-tuned, the model can directly generate materials given desired conditions. Since we use fine-tuning, we only need a small labeled material dataset to generate materials with the corresponding condition, which is very useful for users, because it is often computationally expensive to generate property labels for materials.

Here is an example of how MatterGen generates novel materials in the Sr-V-O (Strontium-Vanadium-Oxygen) chemical system. It generates candidates with lower energy than two other competing methods: random structure search and substitution. The resulting structures look quite reasonable and are proven to be stable using computational methods. 

MatterGen can also generate materials given desired magnetic, electronic, and mechanical properties. The most impressive result here is that we can shift the distribution of generated materials toward extreme values compared with training property distribution. This is very significant, because most materials design problems involve finding materials with extreme properties, such as finding super hard materials or magnets with high magnesium, which is difficult with traditional search-based methods. 

Our next major step is to use these generative AI designed materials to make real-world impacts in a variety of domains, such as battery design, solar-cell design, and carbon capture. One limitation is that we have only validated these AI-generated materials with computation. We are working with experimental partners to synthesize them in the lab. This is not a trivial process, but we will keep improving our models with feedback from the experimentalists. We look forward to a future where generative AI can disrupt the current materials design process and find revolutionary materials that can positively change everyone’s life.

Related resources

• Research Lab Microsoft Research AI for Science 
• Publication MatterGen: a generative model for inorganic materials design 
• Blog MatterGen: Property-guided materials design 
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