Tag: Medicine

Researchers achieve success in allowing a patient to “speak” using only the power of thought

A scientific breakthrough by researchers from Tel Aviv University and Tel Aviv Sourasky Medical Center (Ichilov Hospital) has demonstrated the potential for speech by a silent person using the power of thought only. In an experiment, a silent participant imagined saying one of two syllables. Depth electrodes implanted in his brain transmitted the electrical signals to a computer, which then vocalized the syllables.

The study was led by Dr. Ariel Tankus of Tel Aviv University’s School of Medical and Health Sciences and Tel Aviv Sourasky Medical Center (Ichilov Hospital), along with Dr. Ido Strauss of Tel Aviv University’s School of Medical and Health Sciences and director of the Functional Neurosurgery Unit at Ichilov Hospital. The results of this groundbreaking study were published in the prestigious journal Neurosurgery, the official publication of the Congress of Neurological Surgeons. These findings offer hope for enabling people who are completely paralyzed — due to conditions such as ALS, brainstem stroke, or brain injury — to regain the ability to speak voluntarily.

Dr. Ariel Tankus of Tel Aviv University’s School of Medical and Health Sciences and Tel Aviv Sourasky Medical Center (Ichilov Hospital).

Dr. Ido Strauss of Tel Aviv University’s School of Medical and Health Sciences and director of the Functional Neurosurgery Unit at Ichilov Hospital. Photo credit: Lior Zur, Tel Aviv Sourasky Medical Center (Ichilov Hospital).

How Brain Implants Enable Silent Speech

“The patient in the study is an epilepsy patient who was hospitalized to undergo resection of the epileptic focus in his brain”, explains Dr. Tankus. “to do this, of course, you need to locate the focal point, which is the source of the ‘short’ that sends powerful electrical waves through the brain. This situation pertains to a smaller subset of epilepsy patients who do not respond well to medication and require neurosurgical intervention, and an even smaller subset of epilepsy patients whose suspected focus is located deep within the brain, rather than on the surface of the cortex. To identify the exact location, electrodes have to be implanted into deep structures of their brains. They are then hospitalized, awaiting the next seizure. When a seizure occurs, the electrodes will tell the neurologists and neurosurgeons where the focus is, allowing them to operate precisely. From a scientific perspective, this provides a rare opportunity to get a glimpse into the depths of a living human brain. Fortunately, the epilepsy patient hospitalized at Ichilov agreed to participate in the experiment, which may ultimately help completely paralyzed individuals to express themselves again through artificial speech”.

An image from the experiment of the speech neuroprosthesis (a.k.a speech brain-computer interface). It shows the participant who is completely silent, with his mouth closed, imagining saying a syllable. The laptop “says” the syllable for him.

In the first stage of the experiment, with the depth electrodes already implanted in the patient’s brain, the Tel Aviv University researchers asked him to say two syllables out loud: /a/ and /e/. They recorded the brain activity as he articulated these sounds. Using deep learning and machine learning, the researchers trained artificial intelligence models to identify the specific brain cells whose electrical activity indicated the desire to say /a/ or /e/. Once the computer learned to recognize the pattern of electrical activity associated with these two syllables in the patient’s brain, he was asked only to imagine that he was saying /a/ and /e/. The computer then translated the electrical signals and played the pre-recorded sounds of /a/ or /e/ accordingly.

Decoding the Language of the Brain

“My field of research deals with the encoding and decoding of speech, that is, how individual brain cells participate in the speech process — the production of speech, the hearing of speech, and the imagination of speech, or ‘speaking silently”, says Dr. Tankus. “In this experiment, for the first time in history, we were able to connect the parts of speech to the activity of individual cells from the regions of the brain from which we recorded. This allowed us to distinguish between the electrical signals that represent the sounds /a/ and /e/. At the moment, our research involves two building blocks of speech, two syllables. Of course, our ambition is to get to complete speech, but even two different syllables can enable a fully paralyzed person to signal ‘yes’ and ‘no.’ For example, in the future, it will be possible to train a computer for an ALS patient in the early stages of the disease, while they can still speak. The computer would learn to recognize the electrical signals in the patient’s brain, enabling it to interpret these signals even after they lose the ability to move their muscles. And that is just one example. Our study is a significant step toward developing a brain-computer interface that can replace the brain’s control pathways for speech production, allowing completely paralyzed individuals to communicate voluntarily with their surroundings once again”.

The study was supported by a grant from the Israel Ministry of Innovation, Science and Technology.

New study tracks step length for neurological disease and aging

Researchers at Tel Aviv University and Ichilov’s Tel Aviv Sourasky Medical Center led a multidisciplinary international study in which an innovative model based on machine learning was developed to accurately estimate step length. The new model can be integrated into a wearable device that is attached (with “skin tape”) to the lower back and enables continuous monitoring of steps in a patient’s everyday life. “Step length is a sensitive measure of a wide range of problems and diseases, from cognitive decline and aging to Parkinson’s. The conventional measuring devices that exist today are stationary and cumbersome and are only found in specialized clinics and laboratories. The model we developed enables accurate measurement in a patient’s natural environment throughout the day, using a wearable sensor”, according to the researchers.

The study was led by Assaf Zadka, a graduate student in the Department of Biomedical Engineering at Tel Aviv University; Prof. Jeffrey Hausdorff from the Department of Physical Therapy at the Faculty of Medical and Health Sciences and the Sagol School of Neuroscience at Tel Aviv University, as well as from the Department of Neurology, Tel Aviv Sourasky Medical Center (TASMC); and Prof. Neta Rabin from the Department of Industrial Engineering at the Fleischman Faculty of Engineering at Tel Aviv University. Also participating in the study were Eran Gazit from TASMC, Prof. Anat Mirelman from the Faculty of Medical and Health Sciences and the Sagol School of Neuroscience at Tel Aviv University and TASMC, as well as researchers from Belgium, England, Italy, Holland, and the USA. The research was supported by the Center for AI and Data Science of Tel Aviv University. An article describing the research was published in the journal Digital Medicine.

A person walking in a state-of-the-art gait lab, with a wearable sensor positioned on his lower back. He is walking over a gait mat embedded with force-sensitive sensors. The gait mat provides highly accurate measures of the person’s step length and is used to provide reference values for “training” and testing of the machine learning model described in this study. In the background, specialized motion capture cameras can also be seen. The gait mat and cameras very accurately measure a subject’s walking pattern, over a few steps. However, these tools cannot be used in the real-world, everyday setting. In contrast, the wearable sensor is small, lightweight, and waterproof, can be held in place using skin tape, and can measure step length throughout the day, informing the evaluation of real-world walking and functional abilities.

Can Our Steps Reveal Neurological Health?

Prof. Hausdorff, an expert in the fields of walking, aging, and neurology, explains: “Step length is a very sensitive and non-invasive measure for evaluating a wide variety of conditions and diseases, including aging, deterioration as a result of neurological and neurodegenerative diseases, cognitive decline, Alzheimer’s, Parkinson’s, multiple sclerosis, and more. Today it is common to measure step length using devices found in specialized laboratories and clinics, which are based on cameras and measuring devices like force-sensitive gait mats. While these tests are accurate, they provide only a snapshot view of a person’s walking that likely does not fully reflect real-world, actual functioning. Daily living walking may be influenced by a patient’s level of fatigue, mood, and medications, for example. Continuous, 24/7 monitoring like that enabled by this new model of step length can capture this real-world walking behavior”.

Smart Sensors for Accurate Steps

Prof. Rabin, an expert in machine learning, adds: “To solve the problem, we sought to harness IMU (inertial measurement unit) systems – light and relatively cheap sensors currently installed in every phone and smart-watch, and measure parameters associated with walking. Previous studies have examined IMU-based wearable devices to assess step length, but these experiments were only performed on healthy subjects without walking difficulties, were based on a small sample size that did not allow for generalization, and the devices themselves were not comfortable to wear and sometimes several sensors were needed. We sought to develop an efficient and convenient solution that would suit people with walking problems, such as the sick and the elderly, and would allow quantifying and collecting data on step length, throughout the day, in an environment familiar to the patient. The goal was to develop an algorithm that is capable of translating the IMU data into an accurate assessment of step length, which can be integrated into a wearable and comfortable device”.

To develop the algorithm, the researchers used IMU sensor-based gait data, in addition to step length data measured conventionally in a previous study, from 472 subjects with different conditions, such as Parkinson’s, people with mild cognitive impairment, healthy elderly subjects, as well as younger, healthy adults and people with multiple sclerosis. An accurate and diverse database consisting of 83,569 steps was collected in this way. The researchers used this data and machine learning methods to train several computer models that translated the IMU data into an estimate of step length. To test the robustness of the models the researchers then determined to what extent the various models could accurately analyze new data that was not used in the training process – an ability known as generalization.

New Model Improves Step Length Precision

Assaf Zadka: “We found that the model called XGBoost is the most accurate, and is 3.5 times more accurate than the most advanced biomechanical model currently used to estimate step length. For a single step, the average error of our model was 6 cm – compared to 21 cm predicted by the conventional model. When we evaluated an average of 10 steps, we arrived at an error of less than 5 cm – a threshold known in the professional literature as ‘the minimum difference that has clinical importance’, which allows identifying a significant improvement or decrease in the subject’s condition. In other words, our model is robust and reliable, and can be used to analyze sensor data from subjects, some with walking difficulties, who were not included in the original training set”.

Prof. Hausdorff concludes: “In our research, we collaborated with researchers in diverse fields around the world, and the multi-disciplinary effort led to promising results. We developed a machine learning model that can be integrated with a wearable and easy-to-use sensor, which gives an accurate estimate of the patient’s step length during daily life. The data collected in this way enables continuous, remote, and long-term monitoring of a patient’s condition, and can also be used in clinical trials to examine the effectiveness of medications. Based on our encouraging results, we are looking into whether it is possible to develop similar models based on data from sensors in smart-watches, which would further improve the comfort of the subject”.

Existing Meds May Prevent Bone Spread in Breast Cancer Patients

Researchers at TAU developed a new therapeutic strategy based on existing medications to inhibit bone metastasis in breast cancer patients. Using both an animal model and tissue samples from patients in Israel and the US, they demonstrated that a combination of drugs already available on the market can hinder bone metastasis and improve survival. Based on their findings, the researchers predict that in the future, the treatment can apply to human patients with breast cancer, as well as other types of cancer.

The groundbreaking study was led by Prof. Neta Erez and Dr. Lea Monteran at Prof. Erez’s Laboratory for Tumor Biology at the Pathology Department, Faculty of Medical and Health Sciences, Tel Aviv University. The paper was published in Cancer Discovery. The researchers explain that over 75% of patients with metastatic breast cancer suffer from bone metastases, which destroy bone tissues, causing fractures and a great deal of pain. Moreover, with today’s technologies such as MRI or CT imaging, diagnosis of bone metastasis occurs, in most cases, when the disease cannot be cured.  In this study, the researchers looked for a novel way to inhibit the progression of bone metastasis. 

Cancer Cell Sabotage

Prof. Erez: “A tumor is more than a collection of cancer cells. Just like healthy tissues, a tumor is a whole ecosystem consisting of reciprocal interactions between different cell types, including cells of the immune system, connective tissues, blood vessels, etc. Moreover, cancer cells often ‘corrupt’ normal cells, causing them to ‘collaborate’ with the tumor and support the growth of cancer cells. Blocking the communication channels between cancer cells and healthy cells at an early stage can hinder the growth of cancer cells in the bones. To achieve this, the early stages of the process must be investigated”. To understand processes of bone metastasis the researchers compared three types of bones from model mice:  healthy, early-stage metastasis, and advanced metastasis. They found that when bone metastasis begins, T cells from the immune system arrive on the scene and penetrate the metastases but are unable to destroy them. 

Prof. Neta Erez

Next, the researchers discovered that the killing activity of T cells is inhibited by another type of immune cells and identified the proteins responsible for this effect. To neutralize these inhibitory proteins and reactivate the T-cells, they created a novel therapeutic combination that has never been tried before a drug that counters the activity of the immune-inhibiting cells, along with an antibody that activates T cells. This combination was administered to model mice, and the results were encouraging: the bone metastases were reduced, and survival was significantly improved compared to untreated model mice. 

At the final stage of the study, the TAU research team collaborated with the Sheba and Ichilov (Tel Aviv) Medical Centers and the Baylor College of Medicine in Texas. They examined tissue samples from bone metastases taken from patients with breast cancer, as well as other types of cancer, and found that the immune cells inhibiting T cells express the same proteins as those found in the animal model. Prof. Erez: “Our findings suggest that the combined treatment – attacking the cells that inhibit T cells while activating the T cells – can be effective for treating bone metastasis resulting from breast cancer as well as other types of cancer. The great advantage of our strategy is that both drugs are already available on the market and consequently, the process of obtaining permits to use them against bone metastasis in humans can be relatively short. At the same time, clinical trials need to verify the effectiveness of the new therapeutic strategy”.

The study was funded by the Israel Cancer Research Fund (ICRF), the Israel Science Foundation (ISF), Worldwide Cancer Research (WWCR), and the U.S. Department of Defense (DoD).

A Recent TAU Study Exposes the Connection Between Heart Disease and Cancer

Grass Is Greener, Lives Are Longer: Nature’s Impact on Heart Patients’ Health.

Unique Study Involving 3,128 Heart Patients

Nature’s Health Check

The researchers conclude: “In this study, we examined the survival of coronary heart patients after undergoing bypass surgery, and found that living in a greener environment is associated with better chances of survival. We hypothesize that there are a variety of reasons for this: in a green environment, people breathe cleaner air and engage in more physical activity, the atmosphere may be calmer, and the quality of life is better overall. The research findings may be particularly relevant to the current period in Israel: implying that exposure to a green environment may be a beneficial factor in recovering from trauma”.

The Faculty of Medical and Health Sciences at Tel Aviv University Receives a $12.67 Million Grant from the Helmsley Charitable Trust to Create a Medical Simulation Training Center.

Exposure to The Sun’s UV Radiation May Have a Positive Effect on Fertility in Women Aged 30-40.

TAU Researchers Successfully Prevent Memory Deterioration in Alzheimer’s in an Animal Model.

In 2022, a team of researchers from the laboratory of Prof. Inna Slutsky from the Faculty of Medical & Health Sciences and the Sagol School of Neuroscience at TAU uncovered a pathological brain phenomenon in an animal model that precedes the first appearance of Alzheimer’s disease symptoms by many years. This is an increased activity in the hippocampus during the states of anesthesia and sleep, which results from damage to the mechanism that stabilizes the neural network.

Breakthrough in Alzheimer’s Study

In the current study published in Nature Communications, Prof. Slutsky’s laboratory team, in collaboration with the Safra Center for Neuroscience at the Hebrew University, found that suppression of neuronal activity in a small nucleus in a specific area of the thalamus (which regulates sleep states) caused a decrease in pathological activity in the hippocampus and prevented the deterioration of the memory in Alzheimer’s in an animal model. The researchers hope that their research will speed up the start of clinical trials in humans, lead to progress in the fields of early detection and prevention of the onset of dementia symptoms in Alzheimer’s disease, and in the field of treating cognitive impairments caused by surgery (POCD – Postoperative Cognitive Dysfunction).

Doctoral Student Shiri Shoob

“As early as 10-20 years before the appearance of the familiar symptoms of memory impairment and cognitive decline, physiological changes slowly and gradually occur within the patients’ brains,” explains the doctoral student who led the study, Shiri Shoob. “There is an accumulation of amyloid-beta deposits and abnormal accumulations of tau protein, a decrease in the volume of the hippocampus, and more. Moreover, about 30% of the people who were found to have a pathology typical of Alzheimer’s disease at postmortem did not develop the typical symptoms of the disease during their lifetime. It seems, then, that the brain has an, admittedly limited, ability to protect itself from the damage of the disease.”

Uncovering Alzheimer’s Protective Mechanisms

The research focused on finding those protective mechanisms that the brain has against the damage from the disease. The researchers found that during sleep – and especially during sleep as a result of general anesthesia – the early symptoms of Alzheimer’s disease, which appear many years before the symptoms of dementia, could be more easily identified. Prof. Inna Slutsky: “Anesthesia reveals pathophysiology in the brain activity in the animal model. We think that there are mechanisms that compensate for that same pathology while awake and thus prolong the pre-symptomatic period of the disease”.

Prof. Inna Slutsky

The researchers identified hyperactivity in the hippocampus – “silent seizures,” which look like an epileptic seizure in terms of brain activity, but do not appear externally – in in an animal model of Alzheimer’s disease.This, compared to the reduced activity in the healthy hippocampus during sleep and anesthesia. To examine potential treatment and prevention measures, the researchers tried a variety of methods, but mainly focused on deep brain stimulation (DBS) using electrical signals to the nucleus reuniens – a small nucleus in the brain that connects the affected hippocampus and the thalamus, which is responsible for sleep regulation.

“When we tried to stimulate the nucleus reuniens at high frequencies, as is done in the treatment of Parkinson’s, for example, we found that it worsened the damage to the hippocampus and the silent epileptic seizures,” said Shoob, “only after changing the stimulation pattern to a lower frequency were we able to suppress the seizures and prevent cognitive impairment. We showed that the nucleus reuniens could completely control these seizures. We could increase or decrease the seizures by stimulating it”.

Decoding Alzheimer’s

Prof. Slutsky adds: “Epidemiological studies indicate a link between aging and a phenomenon called POCD – cognitive problems that arise after surgery under general anesthesia. In young people, the symptoms usually pass very quickly, but in older people, the chance of cognitive impairment increases and it may last a long time. Our research indicates a potential mechanism underlying the phenomenon. We found that suppressing the thalamic nucleus reuniens – by pharmacological or electrical means – successfully prevented both pathological activity in the hippocampus during anesthesia and cognitive impairment following anesthesia.

In addition, we identified a relationship between certain pathological activity in the hippocampus during anesthesia in the presymptomatic phase of Alzheimer’s to memory problems in a more advanced stage of the disease. This indicates a potential for predicting the disease in the dormant state, before the onset of cognitive decline”.

The leader of the study, Shiri Shoob, added: “We saw that no matter what means we used, when we inhibited the neural activity in the nucleus, we also measured a decrease in the pathological activity in the hippocampus during anesthesia”.

The research was led by PhD student Shiri Shoob, with the participation of Nadav Buchbinder, PhD student Ortal Shinikamin, Halit Baeloha, Dr. Tomer Langberg, Dr. Daniel Zarhin, Dr. Ilana Shapira and Dr. Gabriella Braun from Prof. Inna Slutsky’s lab and in collaboration with Dr. Naomi Habib and Or Gold from the Hebrew University.

Researchers from Tel Aviv University develop nanoparticles containg RNA molecules,  similar to those used in the COVID-19 vaccine, that inhibit the ability of cancer cells to divide.