Revolutionizing Brain Diagnostics with Light and AI

 Could our non-existent stroke leadership get this tested for an objective damage diagnosis of both gray and white matter? Or aren't there two functioning neurons anywhere in our stroke medical 'professionals'? 

Revolutionizing Brain Diagnostics with Light and AI

Summary: A new “molecular lantern” technique allows researchers to monitor molecular changes in the brain non-invasively using a thin light-emitting probe. This innovative tool utilizes Raman spectroscopy to detect chemical changes caused by tumors, injuries, or other pathologies without altering the brain beforehand.

Unlike prior methods requiring genetic modifications, this approach analyzes natural brain tissue with high precision, offering significant potential for diagnosing and studying brain diseases. Future developments aim to integrate artificial intelligence to enhance diagnostic accuracy and explore diverse biomedical applications.

Key Facts:

• Non-Invasive Monitoring: The molecular lantern uses Raman spectroscopy to detect brain changes without altering tissue.
• Advanced Diagnostics: It identifies molecular changes in pathologies like tumors and trauma with high precision.
• AI Integration: Artificial intelligence could enhance diagnostic markers and classify pathological entities.

Source: CSIC

Monitoring the changes caused in the brain at the molecular level by cancer and other neurological pathologies in a non-invasive way is one of the great challenges of biomedical research.

A new technique, still in the experimental stage, achieves this by introducing light into the brains of mice using a very thin probe.

The innovation, which is published today in the journal Nature Methods, is ledby an international team including groups from the Spanish National Research Council (CSIC) and the Spanish National Cancer Research Centre (CNIO).

The new molecular lantern is based on a technique called vibrational spectroscopy, which leverages the Raman effect—a unique property of light. Credit: Neuroscience News

The authors refer to the new technique as a ‘molecular lantern’, as it provides information on the chemical composition of brain tissue in response to light. This allows analysis of molecular changes caused by tumours, whether primary or metastatic, and also by injuries such as head trauma.

The molecular lantern is a probe of less than 1 mm thick, with a tip just a thousandth of a millimetre (a micron), invisible to the naked eye. It can be inserted deep into the brain without causing damage (as an example, a human hair measures between 30 and 50 microns in diameter).

The probe is not yet ready for use in patients; for now it is primarily a ‘promising’ research tool in animal models that allows ‘monitoring molecular alterations caused by traumatic brain injury, as well as detecting diagnostic markers of brain metastasis with high accuracy,’ explain the authors of the paper.

The work has been carried out by the European consortium NanoBright, in which two Spanish groups participate, the one led by Liset Menéndez de la Prida at the Neuronal Circuits Laboratory of the Cajal Institute of the CSIC, and the Brain Metastasis Group of the CNIO directed by Manuel Valiente. Both have been involved in the biomedical research on NanoBright, while groups from Italian Institute of Technology and French institutions such as the Laboratoire Kastler Brossel, have developed the instrumentation.

Scanning the brain with light without altering it previously

Activating or recording brain function using light is not new. For example, the so-called optogenetic technique make it possible to monitor the activity of individual neurons with light. However, this requires introducing a gene into the neurons that makes them sensitive to light.

With the new technology now presented by NanoBright, it is possible to study the brain without altering it beforehand, which represents a paradigm shift in biomedical research.

The new molecular lantern is based on a technique called vibrational spectroscopy, which leverages the Raman effect—a unique property of light.

“When light interacts with molecules, it scatters in a way that depends on their composition and chemical structure.

“This scattering produces a distinct signal, or spectrum, that acts as a molecular fingerprint, providing detailed information about the composition of the illuminated tissue”, explain Liset M. de la Prida from CSIC.

“We can see any molecular change produced in the brain by a pathology or injury”

“This technology allows us to study the brain in its natural state; it is not necessary to alter it beforehand.

“But it also makes it possible to analyse any type of brain structure, not only those that have been genetically marked or altered, as was the case with the technologies used until now.

“With vibrational spectroscopy we can see any molecular change in the brain when there is a pathology”, explains Manuel Valiente, from the CNIO.

Raman spectroscopy is already used in neurosurgery, although in an invasive and less precise way.

“There have been studies of its use when operating on brain tumours in patients,’ says Valiente. In the operating room, once the bulk of the tumour has been surgically removed, it is possible to introduce a Raman spectroscopy probe to assess whether cancer cells remain in the area.

“That is, it is only used when the brain is already open and the hole is large enough. But these relatively large ‘molecular lanterns’ are incompatible with minimally invasive use in live animal models”.

For the CNIO group, one goal now is to find out whether the information provided by the probe allows “differentiating different oncological entities, for example, the types of metastases according to their mutational profiles, by their primary origin or from different types of brain tumours”.

Artificial intelligence to search for diagnostic markers

The Cajal Institute group has used the technique to investigate the epileptogenic zones surrounding traumativc brain injuries.

“We were able to identify different vibrational profiles in the same brain regions susceptible to epileptic seizures, depending on their association with a tumour or trauma.

“This suggests that the molecular shadows of these areas are affected differently, and can be used to separate different pathological entities by automatic classification algorithms including artificial intelligence”, explains Liset M. de la Prida.

“The integration of vibrational spectroscopy with other modalities for recording brain activity and advanced computational analysis with artificial intelligence will allow us to identify new high-precision diagnostic markers, which will facilitate the development of advanced neurotechnologies for new biomedical applications”, summarizes the CSIC researcher, Liset M. de la Prida.

About this neurotech, brain cancer, and AI research news

Author: Pilar Quijada
Source: CSIC
Contact: Pilar Quijada – CSIC
Image: The image is credited to Neuroscience News

Original Research: Closed access.
“Vibrational fiber photometry: label-free and reporter-free minimally invasive Raman spectroscopy deep in the mouse brain” by Liset Menéndez de la Prida et al. Nature Methods

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

 So we finally got some research that attempts to OBJECTIVELY determine the damage from the stroke. Now if the proper followup occurs we'll get EXACT REHAB PROTOCOLS that fix such damage and get survivors 100% recovered. At least that is what proper stroke research does; it leads to survivor recovery; NOT biomarkers, predictions or descriptions of damage!

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

• C. O. Muller,
• G. Faity,
• M. Muthalib,
• S. Perrey,
• G. Dray,
• B. Xu,
• J. Froger,
• D. Mottet,
• I. Laffont,
• M. Delorme &
• K. Bakhti 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 188 (2024) Cite this article

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Abstract

Background

Following a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.

Methods

Twenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.

Results and conclusions

Kinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

Background

Due to its prevalence, functional non-recovery of the paretic upper limb (UL) is a critical concern in stroke rehabilitation [1]. UL functional recovery is mainly attributed to plastic reorganization within the human brain [2, 3], and post-stroke patients often demonstrate abnormal brain activation in comparison to healthy individuals. When using the paretic hand, patients with stroke show increased contralesional and ipsilesional sensorimotor network activation compared to healthy individuals [4], as well as increased activations of contralesional primary motor cortex and bilateral premotor and supplementary motor areas [5]. During the process of functional paretic arm recovery, there is a progressive evolution towards a more “normal” lateralization of the primary sensorimotor cortex [6,7,8,9,10], which underlines the potential of monitoring brain reorganization to predict patients’ responses to rehabilitation [11]. Brain reorganization is classically assessed by functional magnetic resonance imaging (fMRI), mostly in the supine position and during moderately functional tasks such as thumb-finger opposition or elbow flexion-extension [12]. To monitor brain activations under more ecological conditions, i.e., during upright, unrestrained, functional tasks, it is possible to use portable brain imagery techniques such as functional near infrared spectroscopy (fNIRS) and functional electroencephalography (fEEG).

The fNIRS method detects variations in blood-oxygen level-dependant response, as in fMRI [13], and can do so under more ecological conditions [14]. FNIRS measures both oxygenated (HbO₂) and deoxygenated (HbR) hemoglobin in the cerebral cortex blood vessels, and has been previously used to measure sensorimotor network activation during UL movements in healthy young adults [15, 16], older healthy adults [16, 17] and stroke patients [18, 19]. In fully UL functional tasks, such as reaching, studies have identified a bilateral sensorimotor cortex (SM1) activation pattern [16, 20]. Nevertheless, to the best of our knowledge, only one recent study investigated SM1 activation in a stroke population using fNIRS during a reaching task under ecological conditions [18]. They found enhanced ipsi/contralesional SM1 activation in the stroke patients despite poorer motor performance in reaching and grasping.

The fEEG method detects direct variations in electrical currents at the scalp due to local electric fields produced by neuronal activity [21]. Event-related power changes within specific frequency bands (alpha-mu – 8 to 13 Hz and beta – 14 to 29 Hz) reflect the balance between excitation and inhibition in the sensorimotor network [22], classically with an event-related desynchronization (ERD, i.e. power decrease) at movement execution and an event-related synchronization (ERS, i.e. power increase) at rest [23]. In patients with stroke, a number of studies have shown a relationship between the magnitude of the ERD in the lesioned hemisphere and the paretic UL function [24,25,26].

Coupling fNIRS and fEEG could provide a better spatio-temporal view of SM1 brain activation patterns in both hemispheres [27]. However, to better understand SM1 activity during fully functional UL tasks, it is important to complement functional brain imaging with kinematic assessments [16]. During forward-reaching tasks, stroke patients often exhibit non-mandatory trunk compensation, i.e. even if they can do with their paretic UL alone, they favour trunk flexion to the detriment of arm use [28, 29]. Unfortunately, this non-use of the paretic UL [30] can lead to maladaptive brain plasticity [31] and hinder functional recovery [32]. Overall, it is now clear that non-mandatory trunk compensation and associated non-use have an impact on the plastic reorganisation of the brain (for a review, see [33]). Thus, investigating how trunk compensation affects SM1 activations during different functional UL tasks (detailed description of UL tasks in Sect. “Experimental design”) may help to understand the mechanisms underlying functional recovery [18].

The primary aim of the present study was to investigate bilateral SM1 activation during functional UL tasks in people with and without stroke. We hypothesised increased SM1 activation in the stroke cohort, both in the ipsilesional and contralesional hemispheres and particularly during performance of the paretic UL. Additionally, we investigated the effect of stroke on the relationship between brain activation patterns and motor performance. Our hypothesis was that individuals in the stroke group would perform worse when using their paretic arm, and that SM1 activation in the injured hemisphere would be positively correlated with task performance.

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

 If there is something here that gets survivors recovered, I don't see it.

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

• Jinuk Kim,
• Eunmi Kim,
• Su-Hyun Lee,
• Gihyoun Lee &
• Yun-Hee Kim 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 115 (2024) Cite this article

• 304 Accesses

• 1 Altmetric

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Abstract

Background

Stroke causes long-term disabilities, highlighting the need for innovative rehabilitation strategies for reducing residual impairments. This study explored the potential of functional near-infrared spectroscopy (fNIRS) for monitoring cortical activation during rehabilitation using digital therapeutics.

Methods

This cross-sectional study included 18 patients with chronic stroke, of whom 13 were men. The mean age of the patients was 67.0 ± 7.1 years. Motor function was evaluated through various tests, including the Fugl–Meyer assessment for upper extremity (FMA-UE), grip and pinch strength test, and box and block test. All the patients completed the digital rehabilitation program (MotoCog^®, Cybermedic Co., Ltd., Republic of Korea) while being monitored using fNIRS (NIRScout^®, NIRx Inc., Germany). Statistical parametric mapping (SPM) was employed to analyze the cortical activation patterns from the fNIRS data. Furthermore, the K-nearest neighbor (K-NN) algorithm was used to analyze task performance and fNIRS data to classify the severity of motor impairment.

Results

The participants showed diverse task performances in the digital rehabilitation program, demonstrating distinct patterns of cortical activation that correlated with different motor function levels. Significant activation was observed in the ipsilesional primary motor area (M1), primary somatosensory area (S1), and contralateral prefrontal cortex. The activation patterns varied according to the FMA-UE scores. Positive correlations were observed between the FMA-UE scores and SPM t-values in the ipsilesional M1, whereas negative correlations were observed in the ipsilesional S1, frontal lobe, and parietal lobe. The incorporation of cortical hemodynamic responses with task scores in a digital rehabilitation program substantially improves the accuracy of the K-NN algorithm in classifying upper limb functional levels in patients with stroke. The accuracy for tasks, such as the gas stove-operation task, increased from 44.4% using only task scores to 83.3% when these scores were combined with oxy-Hb t-values from the ipsilesional M1.

Conclusions

The results advocated the development of tailored digital rehabilitation strategies by combining the behavioral and cerebral hemodynamic data of patients with stroke. This approach aligns with the evolving paradigm of personalized rehabilitation in stroke recovery, highlighting the need for further extensive research to optimize rehabilitation outcomes.

Background

Stroke, which has become increasingly prevalent with the global aging population, poses a significant public health challenge, leading to long-term disabilities and substantial burden on healthcare systems [1]. This condition results from cerebral vascular events that cause brain damage, which manifests in various impairments ranging from motor and cognitive to emotional and linguistic difficulties, substantially affecting individuals’ quality of life [2]. Effective rehabilitation is essential for improving functional recovery, reducing disabilities, and enabling the reintegration of stroke survivors into daily life, thus alleviating the socioeconomic burdens on their families and healthcare systems [3].

To create more engaging and interactive rehabilitation experiences, recent advancements in digital technology have introduced innovative approaches to rehabilitation, employing wearable technology, gamification principles, and virtual reality (VR) systems [4, 5]. These digital rehabilitation programs leverage advanced sensors and data analytics for precise patient assessment and personalized treatment plans [6]. Furthermore, artificial intelligence algorithms process data obtained through digital platforms, optimizing therapeutic outcomes by tailoring rehabilitation programs to individual needs and enhancing treatment effectiveness and personalization [7, 8].

Understanding neuroplasticity, which refers to the brain’s ability to create new neural connections, is fundamental for the development of effective neurorehabilitation strategies [9, 10]. This concept underpins treatments aimed at leveraging neural plasticity for recovery, supported by functional brain imaging research that highlights the brain’s capacity for reorganization and adaptation following a stroke [10]. Despite the recognized benefits of digital technologies in rehabilitation, the detailed relationship between cortical activation and rehabilitation outcomes remains unclear. Notwithstanding its limitations, functional near-infrared spectroscopy (fNIRS) offers advantages such as low cost, portability, and resilience to motion artifacts, making it a promising tool for understanding and using brain activation and plasticity in digital rehabilitation [11, 12].

This preliminary study was conducted to investigate cortical activation during stroke rehabilitation, as indicated by cerebral hemodynamic signals measured through fNIRS during digital rehabilitation. The first objective was to determine the characteristics of brain activation captured by cerebral hemodynamic response signals during digital rehabilitation. The second objective was to determine whether machine learning algorithms can effectively classify brain signals and elucidate patients’ functional status. The third objective was to improve our understanding of the intricate relationship between neurophysiological changes and functional motor performance in digital rehabilitation.

More at link.

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

 I see ABSOLUTELY NOTHING here that will get survivors recovered. Useless.

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

• Jinuk Kim,
• Eunmi Kim,
• Su-Hyun Lee,
• Gihyoun Lee &
• Yun-Hee Kim 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 115 (2024) Cite this article

• 175 Accesses

• 1 Altmetric

• Metrics details

Abstract

Background

Stroke causes long-term disabilities, highlighting the need for innovative rehabilitation strategies for reducing residual impairments. This study explored the potential of functional near-infrared spectroscopy (fNIRS) for monitoring cortical activation during rehabilitation using digital therapeutics.

Methods

This cross-sectional study included 18 patients with chronic stroke, of whom 13 were men. The mean age of the patients was 67.0 ± 7.1 years. Motor function was evaluated through various tests, including the Fugl–Meyer assessment for upper extremity (FMA-UE), grip and pinch strength test, and box and block test. All the patients completed the digital rehabilitation program (MotoCog^®, Cybermedic Co., Ltd., Republic of Korea) while being monitored using fNIRS (NIRScout^®, NIRx Inc., Germany). Statistical parametric mapping (SPM) was employed to analyze the cortical activation patterns from the fNIRS data. Furthermore, the K-nearest neighbor (K-NN) algorithm was used to analyze task performance and fNIRS data to classify the severity of motor impairment.

Results

The participants showed diverse task performances in the digital rehabilitation program, demonstrating distinct patterns of cortical activation that correlated with different motor function levels. Significant activation was observed in the ipsilesional primary motor area (M1), primary somatosensory area (S1), and contralateral prefrontal cortex. The activation patterns varied according to the FMA-UE scores. Positive correlations were observed between the FMA-UE scores and SPM t-values in the ipsilesional M1, whereas negative correlations were observed in the ipsilesional S1, frontal lobe, and parietal lobe. The incorporation of cortical hemodynamic responses with task scores in a digital rehabilitation program substantially improves the accuracy of the K-NN algorithm in classifying upper limb functional levels in patients with stroke. The accuracy for tasks, such as the gas stove-operation task, increased from 44.4% using only task scores to 83.3% when these scores were combined with oxy-Hb t-values from the ipsilesional M1.

Conclusions

The results advocated the development of tailored digital rehabilitation strategies by combining the behavioral and cerebral hemodynamic data of patients with stroke. This approach aligns with the evolving paradigm of personalized rehabilitation in stroke recovery, highlighting the need for further extensive research to optimize rehabilitation outcomes.

Background

Stroke, which has become increasingly prevalent with the global aging population, poses a significant public health challenge, leading to long-term disabilities and substantial burden on healthcare systems [1]. This condition results from cerebral vascular events that cause brain damage, which manifests in various impairments ranging from motor and cognitive to emotional and linguistic difficulties, substantially affecting individuals’ quality of life [2]. Effective rehabilitation is essential for improving functional recovery, reducing disabilities, and enabling the reintegration of stroke survivors into daily life, thus alleviating the socioeconomic burdens on their families and healthcare systems [3].

To create more engaging and interactive rehabilitation experiences, recent advancements in digital technology have introduced innovative approaches to rehabilitation, employing wearable technology, gamification principles, and virtual reality (VR) systems [4, 5]. These digital rehabilitation programs leverage advanced sensors and data analytics for precise patient assessment and personalized treatment plans [6]. Furthermore, artificial intelligence algorithms process data obtained through digital platforms, optimizing therapeutic outcomes by tailoring rehabilitation programs to individual needs and enhancing treatment effectiveness and personalization [7, 8].

Understanding neuroplasticity, which refers to the brain’s ability to create new neural connections, is fundamental for the development of effective neurorehabilitation strategies [9, 10]. This concept underpins treatments aimed at leveraging neural plasticity for recovery, supported by functional brain imaging research that highlights the brain’s capacity for reorganization and adaptation following a stroke [10]. Despite the recognized benefits of digital technologies in rehabilitation, the detailed relationship between cortical activation and rehabilitation outcomes remains unclear. Notwithstanding its limitations, functional near-infrared spectroscopy (fNIRS) offers advantages such as low cost, portability, and resilience to motion artifacts, making it a promising tool for understanding and using brain activation and plasticity in digital rehabilitation [11, 12].

This preliminary study was conducted to investigate cortical activation during stroke rehabilitation, as indicated by cerebral hemodynamic signals measured through fNIRS during digital rehabilitation. The first objective was to determine the characteristics of brain activation captured by cerebral hemodynamic response signals during digital rehabilitation. The second objective was to determine whether machine learning algorithms can effectively classify brain signals and elucidate patients’ functional status. The third objective was to improve our understanding of the intricate relationship between neurophysiological changes and functional motor performance in digital rehabilitation.

More at link.

FUNCTIONAL NEAR-INFRARED SPECTROSCOPY-BASED UPPER EXTREMITY FUNCTION REHABILITATION FOR STROKE SURVIVOR: A REVIEW

So you did research just to tell us better research needs to be done. What a useless shitshow. Until survivors are in charge NOTHING WILL GET BETTER IN STROKE.

FUNCTIONAL NEAR-INFRARED SPECTROSCOPY-BASED UPPER EXTREMITY FUNCTION REHABILITATION FOR STROKE SURVIVOR: A REVIEW

JOO-HYUN LEE

and

YOUNG-JIN JUNG

https://doi.org/10.1142/S0219519420500013Cited by:0

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Abstract

Recently, the functional near-infrared spectroscopy (600–900

nm electromagnetic wave) (f-NIRS)-based rehabilitation researches have been studied for understanding the human brain. Although f-NIRS can successfully measure the relative blood concentration changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) as an assessment tool to identify significant clinical intervention during pre- and post-rehabilitation therapy for stroke survivors, there is insufficient information particularly on the use of f-NIRS as a clinical translation in upper extremity function rehabilitation. In order to widely utilize the f-NIRS for upper extremity rehabilitation, device information, experiment design, measurement procedure, and analyzing method are described for clinician aspect in this study. In addition, further research trend was introduced from previous studies for stroke survivor rehabilitation. The authors believed that the information provided in this study can be a useful guideline to encourage future researchers to focus on upper extremity function rehabilitation of stroke survivors.

Interpreting Prefrontal Recruitment During Walking After Stroke: Influence of Individual Differences in Mobility and Cognitive Function

I would prefer that you interpret this in a useful way. Creating stroke rehab protocols. 

Interpreting Prefrontal Recruitment During Walking After Stroke: Influence of Individual Differences in Mobility and Cognitive Function

Sudeshna A. Chatterjee^1,2, Emily J. Fox^2,3, Janis J. Daly^1,4, Dorian K. Rose^1,2, Samuel S. Wu⁵, Evangelos A. Christou⁶, Kelly A. Hawkins², Dana M. Otzel^1,7, Katie A. Butera^1,2, Jared W. Skinner⁸ and David J. Clark^1,7*

• ¹Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center, Gainesville, FL, United States
• ²Department of Physical Therapy, University of Florida, Gainesville, FL, United States
• ³Brooks Rehabilitation, Jacksonville, FL, United States
• ⁴Department of Neurology, University of Florida, Gainesville, FL, United States
• ⁵Department of Biostatistics, University of Florida, Gainesville, FL, United States
• ⁶Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
• ⁷Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States
• ⁸Geriatric Research, Education and Clinical Center, Malcom Randall VA Medical Center, Gainesville, FL, United States

Background: Functional near-infrared spectroscopy (fNIRS) is a valuable neuroimaging approach for studying cortical contributions to walking function. Recruitment of prefrontal cortex during walking has been a particular area of focus in the literature. The present study investigated whether task-related change in prefrontal recruitment measured by fNIRS is affected by individual differences in people post-stroke. The primary hypotheses were that poor mobility function would contribute to prefrontal over-recruitment during typical walking, and that poor cognitive function would contribute to a ceiling in prefrontal recruitment during dual-task walking (i.e., walking with a cognitive task).
Methods: Thirty-three adults with chronic post-stroke hemiparesis performed three tasks: typical walking at preferred speed (Walk), serial-7 subtraction (Serial7), and walking combined with serial-7 subtraction (Dual-Task). Prefrontal recruitment was measured with fNIRS and quantified as the change in oxygenated hemoglobin concentration (ΔO₂Hb) between resting and active periods for each task. Spatiotemporal gait parameters were measured on an electronic walkway. Stepwise regression was used to assess how prefrontal recruitment was affected by individual differences including age, sex, stroke region, injured hemisphere, stroke chronicity, 10-meter walking speed, balance confidence measured by Activities-specific Balance Confidence (ABC) Scale, sensorimotor impairment measured by Fugl-Meyer Assessment, and cognitive function measured by Mini-Mental State Examination (MMSE).
Results: For Walk, poor balance confidence (ABC Scale score) significantly predicted greater prefrontal recruitment (ΔO₂Hb; R² = 0.25, p = 0.003). For Dual-Task, poor cognitive function (MMSE score) significantly predicted lower prefrontal recruitment (ΔO₂Hb; R² = 0.25, p = 0.002).
Conclusions: Poor mobility function predicted higher prefrontal recruitment during typical walking, consistent with compensatory over-recruitment. Poor cognitive function predicted lower prefrontal recruitment during dual-task walking, consistent with a recruitment ceiling effect. These findings indicate that interpretation of prefrontal recruitment should carefully consider the characteristics of the person and demands of the task.

New technology for measuring brain blood flow with light

Don't worry, nothing will become of this for decades since we have NO stroke leadership in any sense of understanding. 

New technology for measuring brain blood flow with light 

University of California - Davis

 

IMAGE: UC Davis biomedical engineers have developed a new technique that could be used for measuring blood flow in the brains of patients with stroke or traumatic brain injury. Light shined... view more 

Credit: Srinivasan lab, UC Davis

Biomedical engineers at the University of California, Davis, have developed a new technique for measuring blood flow in the human brain, which could be used in patients with stroke or traumatic brain injury, for example. The new technique, based on conventional digital camera technology, could be significantly cheaper and more robust than prior methods.

The work is described in a paper published April 26 in the journal Optica.

"Our setup is very promising, and the cost should be lower," said Wenjun Zhou, a postdoctoral researcher working with Vivek Srinivasan, associate professor at the UC Davis Department of Biomedical Engineering.

If you shine a light into a cloudy solution, light particles, or photons, will be scattered in different directions. An experimental technique called diffuse correlation spectroscopy, or DCS, uses essentially this approach to look inside someone's skull. Laser light is shined on the head; as photons from the laser pass through the skull and brain, they are scattered by blood and tissue. A detector placed elsewhere on the head, where the photons make their way out again, picks up the light fluctuations due to blood motion. These fluctuations provide information about blood flow.

The light signal is very weak, and the further it passes through the skull and brain tissue, the weaker it gets. So DCS requires a number of very sensitive, expensive single photon counting detectors. Boosting the light going in risks burning the patient's skin.

Interference to boost signal

Zhou and Srinivasan took a different approach, based on the fact that overlapping light waves will reinforce or cancel each other out, like overlapping ripples on a pond.

They first split the light beam into "sample" and "reference" paths. The sample beam goes into the patient's head and another, stronger, reference beam is routed so that it reconnects with the sample beam before going to the detector. This boosts the signal, meaning that instead of needing about 20 photon-counting detectors that cost a few thousand dollars each, the researchers could use a single CMOS-based digital camera chip for a fraction of the price.

"The strong reference light enhances the weaker signal from the sample," Zhou said.

They call the method interferometric diffusing wave spectroscopy, or iDWS. An added advantage is that they do not need to turn off the room lights while making measurements with iDWS, Zhou said. Eventually, they may even be able to monitor brain blood flow outdoors, under bright sunlight.

So far, the team has tested their device by making brain recordings from volunteers in the laboratory. They are working with Dr. Bruce Lyeth and Dr. Lara Zimmermann in the UC Davis Department of Neurological Surgery to validate and adapt the technology for eventual use in neurocritical care. UC Davis has applied for a provisional patent on the technology.

###

Other authors on the paper are graduate student Oybek Kholiqov and postdoctoral researcher Shau Poh Chong. Srinivasan also holds an appointment at the Department of Ophthalmology and Vision Science, UC Davis School of Medicine. The work was funded by grants from the National Institutes of Health.