Use the labels in the right column to find what you want. Or you can go thru them one by one, there are only 33,685 posts. Searching is done in the search box in upper left corner. I blog on anything to do with stroke. DO NOT DO ANYTHING SUGGESTED HERE AS I AM NOT MEDICALLY TRAINED, YOUR DOCTOR IS, LISTEN TO THEM. BUT I BET THEY DON'T KNOW HOW TO GET YOU 100% RECOVERED. I DON'T EITHER BUT HAVE PLENTY OF QUESTIONS FOR YOUR DOCTOR TO ANSWER.
Changing stroke rehab and research worldwide now.Time is Brain!trillions and trillions of neuronsthatDIEeach day because there areNOeffective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.
What this blog is for:
My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.
Wellumio has closed an oversubscribed US$9.75m round to advance its portable brain imaging system for stroke assessment at the point of care.
The financing round was led by Nuance Connected Capital, with participation from Icehouse Ventures, NZ Growth Capital Partners’ Aspire Seed Fund, Pacific Channel, Booster, Cure Kids Ventures, Flying Kiwi and Enterprise Angels.
The New Zealand-based company said the pre-Series A financing will support clinical validation, regulatory activities and preparations for commercialisation.
Dr Shieak Tzeng, cofounder and chief executive of Wellumio, said: “Every minute counts in stroke care, yet only a small proportion of patients receive treatment within the ‘golden hour’, when timely intervention offers the best chance of reducing long-term disability.”
Wellumio said the device weighs about 50kg and is intended to operate without contrast agents, cryogenics or shielded rooms.
This could allow brain imaging to be carried out in emergency departments and other acute care settings.
However, the technology remains in clinical development and has not received regulatory approval.
Axana is based on Wellumio’s Pulsed Gradient Free Mapping technology, which received a US patent in December 2025.
The company said preclinical studies and first-in-human feasibility trials showed agreement with conventional MR imaging across several stroke biomarkers.
Biomarkers are measurable signs that can help clinicians assess a medical condition.
Summary: A major technological leap has shattered a long-standing limitation in behavioral neuroimaging. Researchers introduced Neuroplex, an imaging pipeline capable of simultaneously tracking the real-time functional activity of up to nine distinct neuronal populations in freely moving mice.
By integrating lightweight, head-mounted miniscopes with high-end spectral confocal microscopy and a custom Python-based alignment tool, the pipeline maps specific genetic or circuit identities directly onto functional brain records. This open-source framework transforms how scientists analyze complex neural computations, offering an unprecedented tool for longitudinal studies of learning, aging, and neurodegenerative disease progression.
Key Facts
Overcoming the Two-Color Limit: Traditional head-mounted miniscopes can record neural activity in behaving animals but lack the spectral capability to differentiate more than two color-coded cell types at a time, forcing slow, animal-to-animal iterative testing.
The Neuroplex In-Vivo Solution: The new pipeline leaves the animal’s brain tissue intact. It records broad neural activity via a miniscope during behavior, removes the scope, and immediately uses a specialized confocal microscope (ZEISS LSM 980) to decode up to nine fluorescent tags through the exact same implanted lens.
Automated Spatial Co-Registration: Developed alongside MetaCell data scientists, Neuroplex uses anatomical landmarks and a custom Python-based alignment script to seamlessly match and overlay the functional miniscope footage with the multicolor confocal identity map.
High-Throughput Validation: As a proof of principle, the team targeted nine distinct projection circuits branching out from the medial prefrontal cortex during social behavior. The automated program successfully assigned roughly 75% of active neurons to their specific circuit identity with 90% accuracy.
Longitudinal Tracking Power: Because the entire alignment procedure is performed non-destructively within the living animal, researchers can identify cell populations and monitor the exact same neurons across weeks or months to see how circuits warp during learning or disease.Source: MPI Florida
Scientists at the Max Planck Florida Institute for Neuroscience (MPFI), in collaboration with ZEISS and MetaCell, have developed a powerful new imaging pipeline called Neuroplex.
Published in eLife, the technique allows simultaneous monitoring of the activity of up to nine distinct neuronal populations in freely moving mice, dramatically accelerating the pace of scientific exploration into how the brain controls behavior.
The Neuroplex imaging pipeline integrates non-destructive in-vivo miniscope activity data with spectral confocal color-tracking through a custom Python alignment tool, allowing neuroscientists to map nine distinct circuit identities onto real-time behavioral records. Credit: Neuroscience NewsThe Challenge
For years, neuroscientists linking brain activity to behavior have faced a fundamental limitation: miniscopes, the tiny head-mounted microscopes used to observe neural activity in behaving animals, could capture neural activity, but couldn’t reliably distinguish more than two different types of brain cells at a time.
“To understand the brain, we need to link patterns of activity in specific neurons to behavior,” stated lead author Dr. Mary Phillips.
“We can readily use labels to color-code different populations of neurons, but when using miniscopes to correlate neural activity to behavior, we couldn’t distinguish more than two of these populations. This made it difficult to compare the activity across multiple cell types and circuits to understand how specific circuits regulate behavior.”
To work around this, researchers were forced to test one cell type at a time, repeating the same behavioral experiments, but labeling distinct neuron types each time. This iterative process, however, was slow and costly. It also prevented direct comparison of different neuron types within the same animal, muddying conclusions due to differences among individual animals.
As an alternative, scientists delineated different neuron types after the behavioral experiment by removing and slicing brain tissue, color-coding different neuron types, then imaging the processed brain tissue using microscopes that can distinguish multiple colors.
However, matching the cells imaged with a miniscope in a living animal to those in post-mortem, processed brain tissue was challenging and low-throughput, resulting in significant data loss. Additionally, this approach destroyed the ability to track the activity of identified cell types over time to determine how their activity changes with learning, aging, or during disease progression.
The Solution: Neuroplex
To overcome these challenges, the MPFI team, together with collaborators at ZEISS and MetaCell, developed Neuroplex, an imaging pipeline that combines the two complementary imaging approaches in the same living animal. Researchers first label up to nine different neural circuits or cell types using a spectrum of differently-colored fluorescent tags.
They then use a tiny lens and a head-mounted miniscope to record the neural activity of the entire labeled population in freely moving, behaving mice. After miniscope imaging, which cannot distinguish among the fluorescent tags, the miniscope is gently removed, and the mouse is positioned under a confocal microscope capable of distinguishing many different colors.
In this case, scientists used the ZEISS LSM 980, a confocal microscope with spectral detection capabilities to distinguish each of the different color tags. With the confocal microscope, the same neurons visualized with the miniscope are imaged through the same lens, but this time the color-coded tags are visualized, identifying which neurons belong to which specific type.
Finally, the images from the miniscope and the confocal are co-registered using anatomical landmarks and a custom Python-based alignment tool that the scientists developed with MetaCell. The result is that the team can map each neuron’s color identity directly onto its functional activity record.
“As part of MetaCell’s contribution to this project, we helped take the complex data collected and turn it into a practical computational workflow that enables imaging, registration, and analysis with greater accuracy, reproducibility, and confidence.“Neuroplex shows how carefully designed computational tools can help researchers make sense of complex biological imaging data and study multiple neuronal populations at once and over time,” says Dr. Zhe Dong, co-author and Data Scientist at MetaCell.
As proof-of-principle, the researchers retrogradely targeted nine brain regions that receive projections from the medial prefrontal cortex, a brain area important for decision making. This allowed them to use a distinct fluorescent marker to distinguish neurons projecting from the prefrontal cortex to nine other brain regions.
They recorded the activity of the neurons across all nine circuits simultaneously as animals interacted socially, sniffing, approaching and following.
“Neuroplex allowed direct comparison of neural activity patterns across cell circuits during social behavior, overcoming long-standing challenges in miniscope recordings and dramatically expanding the efficiency and reproducibility of data collection,” explains senior author Dr. Ryohei Yasuda.
The scientists found that approximately 75% of active neurons could be assigned to one of the nine specific cell types, and the automated program built to assign a neuron to a specific group performed with 90% accuracy and few false positives.
“Because Neuroplex is performed entirely in the living animal through the same implanted lens, it enables scientists to measure how different populations of neurons change their activity over time.
“Researchers can identify cell populations prior to behavior and monitor the same neurons over weeks or months, enabling studies of learning, aging, and disease progression over time,” described Dr. Phillips.
What Comes Next
The team is already working on even more improvements to the technique to increase the accuracy of color code identification. Additionally, they hope to make Neuroplex accessible to all labs, including those that may not have access to high-end spectral confocal systems.
Their goal is to disseminate this approach widely to the neuroscience community by using standard filter-based widefield microscopes, bring the core benefits of the approach to the entire research community.
“The increase in data collection efficiency for cell-type- or circuit-specific functional data will accelerate our understanding of the neural computations underlying behavior,” says Phillips.
“Beyond basic research, we expect this approach to accelerate understanding of circuit-specific functional changes in disease models, particularly in neurodevelopmental or neurodegenerative disease models, which benefit from longitudinal studies examining disease progression.”
To disseminate the approach, the team has also developed tutorials for scientists who wish to use Neuroplex in their own research. In addition, the approach will be featured in a ZEISS webinar with first author Dr. Mary Philips on July 14th to share the technique and resources with the scientific community. Register here for more details.
Funding:This research was funded by National Institutes of Health Grants R35-NS-116804 (RY) and F32MH120872 (M.L.P.) This content is solely the authors’ responsibility and does not necessarily represent the official views of the funders.
Key Questions Answered:
Q: Why couldn’t scientists just use colored lights to see multiple cell types before this invention?
A: The issue wasn’t the colors themselves; it was the physics of the microscopes. To watch a mouse navigate a social environment, the microscope must be tiny and light enough to sit on its head. These miniature scopes are incredible at capturing fast flashes of neural activity, but they are color-blind, they simply cannot distinguish between five, six, or nine different shades of glowing cells. Scientists could color-code the brain, but the live miniscope video just showed a monochrome blur of firing neurons, masking which cell belonged to which circuit.
Q: How does Neuroplex match the color of a cell to its actual behavioral recording?
A: The pipeline treats the problem like an automated puzzle. First, the miniscope records the un-colored, flashing activity of all neurons while the mouse interacts socially. Then, the miniscope is detached, and the mouse is placed under a powerful ZEISS confocal microscope that can read the full spectrum of colors through the exact same lens. Finally, an automated Python program created with MetaCell maps anatomical landmarks to align the two images perfectly, matching each cell’s color identity to its behavioral record.
Q: How does this pipeline help us understand complex brain conditions like Alzheimer’s?
A: Many brain disorders don’t just damage one type of cell; they slowly disrupt communication across vast, interconnected networks of multiple cell types over time. Previously, because identifying cell types required killing the model and slicing the brain tissue, tracking disease progression in a single animal over time was impossible. Because Neuroplex is completely non-destructive, scientists can track how nine different circuits in the exact same brain gradually degrade or adapt over weeks and months of aging or disease.
Editorial Notes:
This article was edited by a Neuroscience News editor.
Journal paper reviewed in full.
Additional context added by our staff.
About this neurotech and neuroscience research news
Author: Lesley Colgan Source: MPI Florida Contact: Lesley Colgan – MPI Florida Image: The image is credited to Neuroscience News
You described a correlation, but NOTHING HERE will help get survivors recovered! The whole fucking point of stroke research is to get survivors recovered, and this completely FAILED! You're fired!
1Department of Neurology, Weifang People’s Hospital, Weifang, China
2Department of Clinical Laboratory, Weifang People’s Hospital, Weifang, China
Objective: To assess the correlation between the triglyceride glucose (TyG) index and short-term neurological and functional outcomes in patients with acute ischemic stroke (AIS) post-intravenous thrombolysis (IVT).
Methods: This prospective observational study included AIS patients treated with IVT within 4.5 h from symptom onset. The TyG index was calculated using fasting triglyceride and glucose levels. Neurological improvement was evaluated by a reduction in National Institutes of Health Stroke Scale (NIHSS) scores, and functional outcome by modified Rankin Scale (mRS) at discharge. Statistical analysis included correlation and regression analyses.
Results: Among 150 AIS patients, the TyG index significantly correlated with both NIHSS (rho = 0.45, p < 0.01) and mRS (rho = 0.38, p < 0.01) scores at discharge. A higher TyG index was associated with neurological non-improvement (OR = 2.11, p = 0.002) and poor functional outcomes (OR = 1.89, p = 0.005) after adjustment for confounders.
Conclusion: The TyG index is significantly associated with short-term outcomes in AIS patients post-IVT, suggesting its potential as a prognostic marker for stroke severity and recovery. Future studies with larger cohorts are needed to confirm these findings.
Absolutely useless! Nothing here helps survivors recover. You just might want to recover from your stroke when you are the 1 in 4 per WHO that has a stroke!
I'd suggest working on a solution now because after your stroke you will be incapable.
Several
theories have been proposed to explain recovery from stroke. Functional
brain imaging offers an opportunity to evaluate these theories and
visualize recovery after stroke. Functional brain imaging has proven to
be an effective tool to map brain areas activated during a specific
task. This paradigm can extend our understanding of the mechanisms of
motor recovery after stroke. Functional brain imaging tools such as
functional MRI, PET, transcranial Doppler ultrasonography, and
transcranial magnetic stimulation can be used to evaluate motor
activation after stroke. Functional imaging is proving useful in
identifying areas, pathways and mechanisms involved in motor recovery
after stroke. Studies have shown changes in motor organization with
rehabilitation. Functional brain imaging may assist in the selection of
rehabilitation methods that best foster recovery. [Neurol Res 2002; 24: 453-458]
You described a correlation, but NOTHING HERE will help get survivors recovered! The whole fucking point of stroke research is to get survivors recovered, and this completely FAILED! You're fired!
Several theories have been proposed to explain recovery from stroke. Functional brain imaging offers an opportunity to evaluate these theories and visualize recovery
after stroke(But it doesn't provide recovery, soes it?). Functional brain imaging has proven to be an effective
tool to map brain areas activated during a specific task. This paradigm
can extend our understanding of the mechanisms of motorrecovery
after stroke. Functional brain imaging tools such as functional MRI,
PET, transcranial Doppler ultrasonography, and transcranial magnetic
stimulation can be used to evaluate motor activation after stroke. Functional imaging is proving useful in identifying areas, pathways and mechanisms involved in motorrecovery after stroke. Studies have shown changes in motor
organization with rehabilitation. Functional brain imaging may assist
in the selection of rehabilitation methods that best foster recovery. [Neurol Res 2002; 24: 453-458]
Parthasarathy Thirumala, Daniel B. Hier and Pratik Patel Departments of Bioengineeringand Neurology and Rehabilitation, The University of Illinois at Chicago, Chicago, IL, USA Several theories have been proposed to explain recovery from stroke. Functional brain imaging offers an opportunity to evaluate these theories and visualize recovery after stroke. Functional brain imaging has proven to be an effective tool to map brain areas activated during a specifc task. This paradigm can extend our understanding of the mechanisms of motor recovery after stroke. Functional brain imaging tools such as functional MRI, PET, transcranial Doppler ultrasonography, and transcranial magnetic stimulation can be used to evaluate motor activation after stroke. Functional imaging is proving useful in identifying areas, pathways and mechanisms involved in motor recovery after stroke. Studies have shown changes in motor organization with rehabilitation. Functional brain imaging may assist in the selection of rehabilitation methods that best foster recovery. [Neurol Res 2002; 24: 453–458] Keywords: Motor recovery; theories of recovery; stroke rehabilitation; functional brain imaging INTRODUCTION Stroke is the third leading cause of death and the leading cause of disability in the United States. There are multiple etiologies of stroke (atherosclerosis, cerebral embolism, in situ thrombosis, small artery occlusion, and hemorrhage). Strokes differ in vascular territory affected, size, severity, and laterality. Some strokes are cortical, some subcortical, and some both. Strokes may affect primary cortex (e.g. motor or sensory), association cortex (visual, auditory, etc.) or cortex involved in higher cortical functioning. Strokes differ in outcome. Some show a rapid improvement; others show scant improvement. In one study 1 of unselected strokes, 19% were rated very severe, 14% severe, 26% moderate, and 41% mild. In patients surviving the stroke, neurologic impairment after rehabilitation was rated severe or very severe in 11%, moderate in 11%, and mild in 47%. About 31% achieved normal neurologic functioning. In another study 2 , only 45% of stroke survivors were functionally independent at six months. Motor deficits remain a major cause of long-term disability after stroke. Although motor recovery occurs after stroke, it is often incomplete. The most important predictor of recovery is initial severity. After rehabilitation, the group with moderate and severe disability is reduced from 50% to 25% and the group with mild or no disability is increased from 50% to 75%. One third of the survivors in the mild group are discharged back to their homes with little disability 1
. The mechanism by which motor recovery occurs is uncertain. Functional MRI and other functional brain imaging techniques are revealing how motor recovery occurs after stroke. HOW DOES MOTOR RECOVERY OCCUR? A variety of theories have been advanced to explain motor recovery after stroke
3 . These theories are not mutually exclusive and motor recovery may occur by several mechanisms. Some initial motor recovery occurs because of the resolution of initial metabolic disturbances related to ischemia and anoxia. Restitution of cerebral blood flow with resolution of acute anoxia, edema, ischemia, and acidosis correlates with improved neuronal functioning and return of some lost motor function in brain areas that have not undergone frank infarction. Similarly, some early recovery may be due to the resolution of diaschisis. With diaschisis, there may be functional changes in neuronal activity at sites distant from areas of brain injury. Brain areas may become temporarily and reversibly dysfunctional if connected to ischemic areas. The resolution of diaschisis may explain some motor recovery. Nonetheless, functional reorganization must be important to much of the recovery that after stroke. This reorganization can occur around the lesion itself, elsewhere in the same hemisphere, or in the hemisphere contralateral to the stroke 4,5 . The mechanisms of functional reorganization include perilesional remapping (reorganization of motor functions around the site of the stroke), use of collateral pathways in the same hemisphere, or use of collateral pathways in the opposite hemisphere. Functional MRI and other functional brain imaging techniques can show how these mechanisms, either individually, in sequence, or in concert promotes motor recovery after stroke.
The lesson I got from this is that NOTHING HERE is going to get you recovered, the only goal in stroke! Survivors don't care about your imaging, it does nothing for recovery. Send me hate mail on this: oc1dean@gmail.com. I'll print your complete statement with name and my response in my blog.
Several
theories have been proposed to explain recovery from stroke. Functional
brain imaging offers an opportunity to evaluate these theories and
visualize recovery after stroke. Functional brain imaging has proven to
be an effective tool to map brain areas activated during a specific
task. This paradigm can extend our understanding of the mechanisms of
motor recovery after stroke. Functional brain imaging tools such as
functional MRI, PET, transcranial Doppler ultrasonography, and
transcranial magnetic stimulation can be used to evaluate motor
activation after stroke. Functional imaging is proving useful in
identifying areas, pathways and mechanisms involved in motor recovery
after stroke. Studies have shown changes in motor organization with
rehabilitation. Functional brain imaging may assist(If this really helped the statement would be: This imaging gives us an exact damage diagnosis so we now can choose the correct rehab protocols that deliver recovery from such damage.) in the selection of
rehabilitation methods that best foster recovery. [Neurol Res 2002; 24: 453-458]
Good 8:29 video, not going to help you recover from your stroke but will make you more knowledgeable than your doctor!
Oops, I'm not playing by the polite rules of Dale Carnegie, 'How to Win Friends and Influence People'.
Telling supposedly smart stroke medical persons they know nothing about stroke is a no-no even if it is true.
Politeness
will never solve anything in stroke. Yes, I'm a bomb thrower and proud
of it. Someday a stroke 'leader' will try to ream me out for making them look bad by being truthful, I
look forward to that day.
Several
theories have been proposed to explain recovery from stroke. Functional
brain imaging offers an opportunity to evaluate these theories and
visualize recovery after stroke. Functional brain imaging has proven to
be an effective tool to map brain areas activated during a specific
task. This paradigm can extend our understanding of the mechanisms of
motor recovery after stroke. Functional brain imaging tools such as
functional MRI, PET, transcranial Doppler ultrasonography, and
transcranial magnetic stimulation can be used to evaluate motor
activation after stroke. Functional imaging is proving useful in
identifying areas, pathways and mechanisms involved in motor recovery
after stroke. Studies have shown changes in motor organization with
rehabilitation. Functional brain imaging may assist(EXACTLY HOW?) in the selection of
rehabilitation methods that best foster recovery. [Neurol Res 2002; 24: 453-458]
Several
theories have been proposed to explain recovery from stroke. Functional
brain imaging offers an opportunity to evaluate these theories and
visualize recovery after stroke. Functional brain imaging has proven to
be an effective tool to map brain areas activated during a specific
task. This paradigm can extend our understanding of the mechanisms of
motor recovery after stroke. Functional brain imaging tools such as
functional MRI, PET, transcranial Doppler ultrasonography, and
transcranial magnetic stimulation can be used to evaluate motor
activation after stroke. Functional imaging is proving useful in
identifying areas, pathways and mechanisms involved in motor recovery
after stroke. Studies have shown changes in motor organization with
rehabilitation. Functional brain imaging may assist in the selection of
rehabilitation methods that best foster recovery. [Neurol Res 2002; 24: 453-458]
Defeng Hua1
Zhen Guo