Scientists have identified two specific types of brain cells that behave differently in people with depression, offering a clearer picture of what is happening inside the brain. By analyzing donated brain tissue with advanced genetic tools, the researchers found changes in neurons linked to mood and stress, as well as in immune-related microglia cells. These differences point to disruptions in key brain systems and reinforce that depression is rooted in biology, not just emotions.
Researchers at the University of São Paulo (USP) in Brazil discovered that neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and multiple sclerosis, are more complex than previously thought. Their analysis of nearly 600 blood samples from patients with and without these diseases revealed that neurodegenerative processes extend beyond the central nervous system, affecting various targets throughout the body.
“We conducted a systemic analysis based on autoantibodies—defense proteins [immunoglobulins] that mistakenly attack the body’s healthy cells, tissues, or organs instead of external pathogens. In this study, we saw that, contrary to what was previously thought, these diseases don’t involve an antibody attacking only a specific region of the connection between neurons [synapse], like a thief breaking in through a door. It’s a systemic attack, like machine-gunning an entire house,” explains Júlia Nakanishi Usuda, first author of the study.
The study, published in the journal iScience, identified more than 9,000 autoantibodies from public databases. Based on the results, the researchers suggest that, rather than focusing on isolated molecular targets, treatment strategies for these diseases should focus on blocking the autoimmune response systemically. While the data science study still needs to be confirmed through in vitro and in vivo testing, it reinforces a new paradigm for treating neurodegenerative diseases.
A novel strategy that combines computational and experimental approaches has allowed researchers at Baylor College of Medicine and the Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital to distinguish alterations in gene function that contribute to Parkinson’s disease from those that protect from the condition. The study, published in Neurobiology of Disease, revealed novel risk factors and previously unrecognized therapeutic targets, offering hope for a future in which effective therapies will be available to prevent, slow down or stop this devastating disease.
“Parkinson’s disease is the most common neurodegenerative movement disorder—it affects more than 10 million people worldwide,” said corresponding author Dr. Juan Botas, professor of molecular and human genetics and molecular and cellular biology at Baylor. Botas also is a member of the Duncan NRI and director of the High Throughput Behavioral Screening Core at Texas Children’s.
“People with the condition have tremors, muscle stiffness and balance problems. They move slowly with a shuffling gait; their symptoms often start gradually and worsen over the years. Current therapies only relieve symptoms but do not prevent the gradual loss of brain cells called neurons that cause the disease,” said Dr. Botas.
Nothing rivals the human brain’s complexity. Its 86 billion neurons and 85 billion other cells make an estimated 100 trillion connections. If the brain were a computer, it would perform an exaflop (a billion-billion) mathematical calculations every second and use the equivalent of only 20 watts of power. As impressive as the brain is, neurologists can’t fully explain how neurons work together.
To help find answers, researchers at the Institute for Neuroscience, Neurotechnology, and Society (INNS) at Georgia Tech are using math, data, and AI to unlock the secrets of thought. Together they are helping turn the brain’s raw electrical “noise” into real insights about how people think, move, and perceive the world.
Fair warning: Prepare your neurons for the complexity of this brain research ahead.
The Individual Brain Charting (IBC) project has released its fifth and largest update of high-resolution fMRI data, adding a new set of cognitive tasks to one of the most detailed brain-mapping datasets available today. The dataset, which is openly accessible through EBRAINS, is described in a new publication in Nature Scientific Data.
The new release expands the dataset with 18 tasks collected from 11 participants under tightly controlled, standardised conditions – bringing many of them close to 40 hours of scanned data each.
The IBC project launched in 2014 and was funded by the Human Brain Project. It aims to map how individual brains respond across a wide range of cognitive functions. By repeatedly scanning the same participants with diverse tasks – from mathematics and spatial navigation to emotion recognition, reward processing, and working memory – the team is building an exceptionally rich resource for studying individual variability in brain organization.
The aim of this randomized, controlled, double-blind multicenter trial was to compare the safety and efficacy of globus pallidus internus (GPi) and subthalamic nucleus (STN) deep brain stimulation (DBS) in patients with Meige syndrome (MeS). Additionally, the authors explored the optimal site of DBS and identified predictors of clinical outcomes.
The primary outcome was improvement in motor function as assessed by the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS). The secondary outcomes included mood, global cognitive function, and quality of life (QOL). The optimal stimulation site for DBS was investigated using Lead-DBS.
A total of 62 patients with MeS were randomized to receive GPi-DBS (n = 31) or STN-DBS (n = 31), and all completed the 1-year follow-up. In the GPi-DBS group, the mean improvement rates in BFMDRS movement scores were 54.9%, 57.3%, and 59.7% at 3, 6, and 12 months, respectively. In the STN-DBS group, the corresponding rates were 57.1%, 59.0%, and 59.9%. There was no significant difference in the efficacy of motor symptoms, depression, anxiety, and QOL between the two groups during follow-up. The total electrical energy delivered in the GPi-DBS group was significantly greater than that in the STN-DBS group. The adverse event rates were comparable between the GPi-DBS (16.1%) and STN-DBS (12.9%) groups (p 0.99). The “sweet spot” for GPi-DBS was found to be located in the posterolateral dorsal pallidum (ρ = 0.76, p = 0.001), while the sweet spot for STN-DBS was found to be situated in the dorsal subthalamic nucleus (ρ = 0.66, p = 0.005).
OBJECTIVE Magnetic resonance–guided focused ultrasound (MRgFUS) is increasingly recognized as an effective treatment option for patients with medication-refractory essential tremor (ET). Indirect coordinates of the ventral intermediate nucleus of the thalamus, as well as the dentato-rubro-thalamic tract (DRTT) originating from the ipsilateral dentate nucleus, known as the “nondecussating DRTT” (nd-DRTT), are commonly used as targets for sonication. Anatomically, the DRTT originating from the contralateral dentate nucleus, referred to as the “decussating DRTT” (d-DRTT), constitutes the predominant component of the two fiber populations. However, the d-DRTT is rarely visualized using conventional diffusion tensor imaging (DTI) because of the technical challenges associated with resolving crossing fiber orientations. Probabilistic tractography enables the differentiation of crossing fibers, thus allowing for visualization of both the d-DRTT and nd-DRTT. Authors of this study aimed to evaluate whether the d-DRTT delineated by probabilistic tractography represents an anatomical target more important than indirect coordinates or the nd-DRTT. METHODS Consecutive patients with medically refractory ET who underwent unilateral MRgFUS thalamotomy at a single institution between May 2022 and August 2024 were analyzed. Tremor severity was assessed using the Clinical Rating Scale for Tremor Part B, and the percentage improvement at 3 months after treatment was calculated as an indicator of functional recovery. Probabilistic tractography of the DRTT was performed post hoc using preoperative diffusion MRI and Bayesian modeling (BedpostX) and probabilistic tracking (ProbtrackX). The distances between the sonicated lesion as detected on postoperative MRI and each of the following were compared: indirect coordinates, nd-DRTT, and d-DRTT. Subgroup analysis was performed on patients with a peak lesion temperature ≥ 55°C. Pearson correlation was used to assess the relationships between distance metrics and clinical outcomes. RESULTS Probabilistic tractography successfully visualized the d-DRTT in all 28 patients included in the study. The d-DRTT was more lateral than both the indirect coordinate and the nd-DRTT (p < 0.01 for both), with a nonsignificant tendency for a more anterior position relative to the nd-DRTT (p = 0.054). Among the patients with a peak lesion temperature ≥ 55°C, the distance between the sonicated lesion and the d-DRTT showed a strong correlation with clinical outcomes, whereas that between the lesion and nd-DRTT showed a moderate correlation; the indirect coordinates showed no significant correlation. CONCLUSIONS Probabilistic tractography successfully visualized the d-DRTT, and its location appears to capture the “tremor-relevant” neural pathway more accurately than either the indirect coordinate or the nd-DRTT.