Fatigue. And the good news is there are strategies to help minimize it.
Before I get to the strategies, here are a couple of real life descriptions to help explain what it is like:
In an article in The Guardian newspaper Tim Lusher described his experiences following an abscess on the cerebellum, (the part of the brain that controls movement, balance and coordination).He vividly describes cognitive fatigue:
Ah, the tiredness. That’s another thing everyone talks about. It’s not a tough-week-at-the-office tiredness that you can rally through with a couple of drinks and the prospect of a weekend lie-in. It’s a leaden blanket of exhaustion that sweeps over you – utterly undeniable, non-negotiable and unshakeable.
And this description in an article entitled “Learning to Pace Yourself” from Synapse
Those who haven’t had their brain banged around won’t understand the feeling – they’ll picture how they feel after a bad night’s sleep or a big work day. But this mental exhaustion is much more than that. It feels as though even the simple act of pushing a few sluggish thoughts through this damaged brain takes far too much energy, let alone attempting things requiring physical exertion. To make things worse, when I got tired my emotions were worse than ever – my family was already struggling with my temper, depression and poor social skills. What little control I had in these areas just flew out the window once fatigue set in.
Cognitive fatigue is common after a brain injury, whether mild, moderate or severe.
The brain is working harder to keep up all its functions, even ones that were once second nature. Eventually it is like an overload button, the brain needs a rest. Without rest it can lead to headaches, or becoming irritable, confused and sometimes increasing problems with behaviour.
What can you do about it? Well even understanding what it is, gives you clues about how you could assist a person manage it. Here are some ideas to get you started.
What Can You Do for Cognitive Fatigue?
Below is a list of strategies you might find useful to work with. Decide what might work with the person you are supporting and their network. Just choose the key strategies that might suit. Keep the change manageable for everyone involved.
Balance the daily routine with quiet times, rests, or restful activity; building in whatever rest time the person needs whether a short nap or a longer sleep time.
Help family and friends to understand cognitive fatigue and know that it is as a result of the brain damage, it’s not laziness or deliberate.
Plan ahead to allow opportunity for sleep and rest, program this into the daily plan before fatigue occurs.
Work out what time of day is best for activity. We often talk about whether we are a morning, afternoon or evening person, this is important in planning to minimize fatigue.
Allow extra time to complete work that requires extra concentration and effort.
Plan ahead for demanding activities, or when going to special events. Allow for extra rest time and / or quieter routines before and after.
Use aids, equipment, and technology to reduce effort wherever possible. For instance if the person has mobility aids encourage their use to minimize fatigue.
If helpful see about shorter days for school or work; and with frequent breaks according to need.
Encourage saying no to activities or demands that are not important, or that would overly fatigue them.
If there are a number of activities or things to do on a day, work out priorities and tackle the important, or interesting tasks first.
As much as possible have familiar routines and surroundings, which reduces to effort and need to concentrate.
Take notice of what factors contribute to fatigue and work out how to manage these as much as possible. This might include the effect of medication, weather, or illness, people, places.
Be aware that sensory overload can impact on fatigue; situations such as a busy shopping centre with lights and noise. Limit or avoid these situations.
Maintain optimal health and fitness. Take care with exercise that it is does not itself cause fatigue.
Develop ways to manage fatigue if and when it occurs. Think about at home and when out.
You as a supporter can minimize fatigue by assisting where necessary, and where appropriate. Carrying out tasks, understanding what needs to be done, assisting to maintain agreed rest routines.
When looking at ways to manage fatigue remember it is better for a person to try and manage cognitive fatigue before rather than after it happens. Plan to prevent rather than manage after fatigue occurs.
Finally remember to always work with the person and their support team when developing any strategies. Each person will have different needs and different responses. This may change over time. Consistency is a key.
Please share any successful (or unsuccessful ways) you might have seen cognitive fatigue managed.
The last decades have seen major advances in interventions for neuromotor rehabilitation. Forms of treatment based on repetitive exercise of coordinated motor activities have been proved effective in improving gait and arm functions and ultimately the patients’ quality of life. Exercise-based treatments constitute a significant burden for therapists and are heavy consumers of health-care resources. Technologies such as robotics and virtual reality can make them more affordable.
Rehabilitation robotics specifically focuses on systems—devices, exercise scenarios, and control strategies—aimed at facilitating the recovery of impaired sensory, motor, and cognitive skills. The field has a relatively long history, dating back to the early 1990s. Early attempts were part of the general trend toward automating heavy tasks by using “intelligent” machines, with minimal human intervention. The notion of “artificial therapist” was common in early scientific papers and patent applications. However, the most distinctive feature of these devices is not their ability to “automate” treatment but, rather, that of precisely quantifying sensorimotor performance during exercise, in terms of movement kinematics and exchanged forces. This resulted in a gradual shift toward more evidence-based and data-driven forms of treatment. Present-generation rehabilitation robots are designed as complements, rather than substitutes, of the therapist’s work. They support the recovery of functions by efficiently exploiting structure and adaptive properties of the human sensorimotor systems and provide rich information on sensorimotor performance and their evolution. Their design, implementation, and modalities of intervention incorporate findings from behavioral studies on sensorimotor adaptation and motor skill learning and their neural substrates.
Rehabilitation robotics is therefore characterized by highly specific design approaches and technical solutions, with roots in both engineering and neurophysiology.
This book addresses both technology and application aspects of Rehabilitation Robotics. Part I focuses on the state of the art and representative advancements in the design, control, analysis, and implementation of rehabilitation robots and the underlying neurophysiological principles. Part II addresses the existing applications and the clinical validation of these systems, with a special emphasis on therapy robots, which support exercise-based treatments aimed at recovering sensorimotor or cognitive functions.
PART I: Background and Technology
Planning and execution of movements results from the coordinated activity of multiple interconnected sensory and motor areas in the cerebral cortex. When an area in this specialized motor network is damaged—for example, through a traumatic brain injury or an ischemic event—the activity of the motor networks can be disrupted, thus leading to functional deficits. How the surviving motor networks reorganize to compensate for the injury depends on the location and extent of the lesion but may be affected by sensorimotor exercise.
Chapter 1 summarizes how neuroplasticity modifies motor networks in response to injury, by focusing on the changes after a cerebrovascular accident in the primary motor cortex. Neuroanatomical and neurophysiological evidence in animal models and human stroke survivors is reviewed to demonstrate how injuries functionally impair motor networks, how motor networks compensate for the lesion to improve motor function, and how selected therapies may facilitate recovery.
Chapter 2 focuses on the hierarchical architecture and synergistic functioning of the motor system. These aspects are crucial for the development of successful robotics applications with rehabilitation purposes. The same framework is used to discuss the mechanisms underlying rehabilitation interventions with a potential to facilitate the recovery process.
Technology and Design Concepts
Devices for rehabilitation benefit from advances in robot technologies, including sensors and actuators, mechanical architectures, and the corresponding control architectures. These devices are characterized by a continuous interaction with the human body, which poses specific design constraints.
Chapter 3 summarizes the notion of “biomechatronic” design for systems for robot-mediated rehabilitation, encompassing robot structure, musculoskeletal biomechanics, and neural control. Robots for rehabilitation are typically conceived to constantly work in constrained motion with the human body, which represents a challenge for designers. This requires a top-down design approach, in which a model of the human agent guides a concurrent, iterative design cycle of the robot’s mechanical, electronic, and multilayered control subsystems. Criteria for the identification of functional and technical specifications and the selection of key components of the robotic system are also derived. Two design case studies demonstrate how these design principles are translated into practice.
Chapter 4 addresses how actuators play a critical role in defining the characteristics of the robot-patient interaction. The different options for actuating and controlling a rehabilitation device are discussed, considering the complex flow of information between the user and the robot during a rehabilitation task. Strategies for both high- and low-level control are presented. Impedance and admittance control modalities are discussed as means of decoding human intention and/or modulating the assistive forces delivered by the robot. Mathematical tools for model-based compensation of nonlinear phenomena (backlash and friction) are also presented.
The way robots are used to facilitate training is crucial for their application to therapy and has important implications for their mechanical and control design. Intensity and frequency of practice are major determinants of the recovery process, but different exercise modalities are possible. Robots may be used for haptic rendering in virtual environments, to provide forces that facilitate task performance or task completion, and/or to make a task more difficult and challenging.
Chapter 5 reviews the control strategies for robotic therapy devices and summarizes the techniques for implementing assistive strategies, including counterbalance techniques and adaptive controllers that modify control parameters based on the patient’s ongoing performance.
Personalized treatment is becoming increasingly popular in neurorehabilitation. Two chapters discuss how new design techniques such as exoskeletons or wearable robots are applied to the design of modern therapy robots, for either upper or lower limb rehabilitation.
Chapter 6 specifically addresses the design of exoskeletons for upper-limb rehabilitation. After an introduction of the rationale behind the selection of this robot architecture and a review of the available solutions for actuation, the chapter discusses the state of the art and the most commonly adopted solutions. An overview of clinical evidences of upper-limb rehabilitation with exoskeletons is then provided, discussing evidences in favor of training with exoskeleton devices.
Chapter 7 reviews the current state and clinical effectiveness, safety, and usability of exoskeletons for gait rehabilitation. It provides an overview of the actuation technologies, including compliant and lightweight solutions. Control strategies aimed at guiding the patient according to his/her needs and encouraging his/her active participation are also discussed. Novel perspectives for “symbiotic” human-exoskeleton interaction based on interfaces with neural structures are also introduced.
One important feature of therapy robots is that they integrate both therapeutic and measuring functionalities. Therapy robots have built-in technology and sensors that measure movement kinematics and kinetics, thus providing an accurate assessment of motor function by which it is possible to diagnose the patient state and to evaluate patient performance and their progress during treatment. The availability of quantitative information has triggered an entirely new paradigm for neurorehabilitation, unifying clinical assessment, and exercise. Computational neurorehabilitation is a new and emerging field, which uses modern data analysis and modeling techniques to understand the mechanisms of neural plasticity and motor learning, and incorporates this knowledge into personalized, data- and model-driven forms of treatment.
Chapter 8 reviews the quantitative measures—encompassing kinematic, kinetic, timing, sensory, and neuromechanical aspects of performance—which are most frequently used to describe motor behavior during robot-assisted rehabilitation of the upper limb. The chapter also analyzes how these indicators are used to monitor motor recovery during exercise, to understand the evolution of performance, and to precisely plan and, if necessary, modify the rehabilitation strategies. The relationship between robot-derived measures and their clinical counterparts is also discussed.
Chapter 9 addresses computational models for neuromotor recovery, with a focus on state-space models that describe the development of functional behaviors through exercise and the relation between neuromotor recovery and motor learning. The chapter first reviews models of the dynamics of sensorimotor adaptation and motor skill learning and then elaborates on similarities and differences with neuromotor recovery. Finally, it discusses how these models can be used to achieve a better understanding of the role of robots to promote recovery and to develop personalized forms of treatment.
Chapter 10 proposes a general framework to model the interaction between robot and patient during robot-assisted training. Human and robot are modeled as two agents, whose respective tasks are described by two cost functions. Optimal interaction strategies are then derived in terms of differential game theory. This approach allows to describe different forms of human-robot interaction. A specific prediction is that optimal interaction requires that the robot maintains a model of the behavior of its human partner. In this case, simulations and empirical studies exhibit more stable, reactive, and adaptive interaction. This form of “symbiotic” interaction is a step toward defining what it takes for robots to behave as “optimal” trainers.
Chapter 11 addresses the strategies implemented in rehabilitation robots to promote patient motivation, which is a major determinant of recovery through exercise. Motivation may be measured with self-report questionnaires or with indirect, more objective measures, such as exercise duration. Motivation may be promoted through interaction with virtual environments, which may consist of activities of daily living, which emphasize relatability, or games, which emphasize enjoyment. The design of these environments must take the hardware, the patients’ characteristics, and goal-related feedback into account. Motivation during exercise must be maintained by regulating task difficulty, thus ensuring an appropriate “challenge level.”
Software Environments for Rehabilitation Robotics
As a natural conclusion of this methodological section, Chapter 12 reviews the software development environments that can be used to implement the different levels of control of a modern rehabilitation robot. The robotic field suffers from a lack of standardization in programming environments. Hence, it is not surprising that even in the specific context of rehabilitation robotics, there is currently no consensus on specific software and hardware platforms. The chapter surveys different solutions used for combining robots (and, more in general, haptic interfaces) and virtual environments. Advantages and disadvantages of each of these environments are discussed, together with typical applications, with a focus on upper-limb rehabilitation.
PART II: Applications
The second part of the book addresses the application of rehabilitation robots in different pathologies for training of diverse districts (upper and lower limb) and using different training strategies.
High Intensity, Assist-As-Needed Therapy to Improve Motor Functions
Chapter 13 provides an overview of 28 + years of efforts at MIT’s Newman Laboratory for Biomechanics and Human Rehabilitation for the developments of robotic tools to assist in the neurorecovery process. After a definition of the basic principles that are core for successful rehabilitation robotics technology, the chapter presents a snapshot of few of MIT’s rehabilitation robots, discusses the results of metaanalyses for upper extremity robotics, and finally presents two exciting examples for acute and chronic stroke. Overall, the above material points out that robotic therapy for the upper extremity that involves an interactive high-intensity, intention-driven therapy based on motor learning principles and assist-as-needed leads to better outcomes than usual care in both acute/subacute and chronic stroke.
The above principles have been extended to training in a three-dimensional workspace, using robots with an exoskeleton structure. Chapter 14 describes the application of one of the first architectures developed with the purpose of mirroring the anatomical structure of the human arm and of enabling task-oriented training in the 3D space, mimicking activities of daily living.
Hand and finger functions are of critical importance for independence in everyday activities, but their recovery is often limited following neurological injury. This has motivated the development of novel therapeutic and assistive tools. Chapter 15 provides a comprehensive overview of robotic approaches for the rehabilitation of hand function and underlines their potential to complement conventional rehabilitation. First, the design concepts of existing hand exoskeletons and end-effector devices are presented. Then, clinical evidence that underlines the feasibility of robot-assisted rehabilitation of hand function is presented. Finally, promising research directions are discussed to further exploit the potential of robot-assisted rehabilitation of hand function in neurological patients.
Robot-assisted gait training typically involves body-weight support and physical guidance to move the legs into the correct pattern. Gait rehabilitation robots allow greater exercise duration and movement repetitions; improve patient safety and motivation; reduce the therapists’ burden; and, eventually, improve the therapeutic outcome. Chapter 16 introduces the rationale for robot-assisted gait training. In particular, existing gait-rehabilitation robots and their control strategies are presented. The available clinical trials are also summarized, showing that training with robotic rehabilitation devices is at least as effective as conventional physiotherapy. Further clinical studies are required in order to define the most appropriate robotic technical features based on the task, patients’ type, and degree of impairment.
Wearable systems open new perspectives for rehabilitation in individuals with disabilities, which can lead to difficulty in walking or making arm movements since they could be used to facilitate independent training in the clinic or at home. Wearable systems range from complex rigid exoskeleton structures for the assistance of joints or limbs to hybrid, soft, and interactive systems. The existing solutions are not yet widely used in clinical environments. The aim of Chapter 17 is to review the scientific challenges and the current developments of wearable systems and to discuss their clinical potential.
Robots Not Only for Stroke Rehabilitation
Although most applications of robot rehabilitation focus on stroke and traumatic brain injury, these devices may find application in the treatment of other pathologies.
Chapter 18 addresses robot-assisted rehabilitation in multiple sclerosis (MS). Robot-assisted training leads to improved movement quality on reaching tasks, but clinical effects on standard assessment have not been always observed after multiple-session training. An increasing number of studies report effects of a multiple-week training program, but the magnitude of the effect was often similar to conventional training programs. Overall, there is evidence supporting the beneficial effect of robot- and technology-supported training, but its superiority compared with other or conventional treatment programs is still debatable. Research investigating the impact of different technological settings and the motor learning strategies implemented in technology must be encouraged for MS patients.
Persons with cognitive deficits are a completely different target population that can be addressed by therapy robots. Cognitive rehabilitation therapy (CRT) is a set of interventions designed to enhance cognitive performance. Ideally, CRT engages the participant in a learning activity to enhance neurocognitive skills relevant to the overall recovery goals. There is ongoing research to identify the determinants of a positive response to treatment. Chapter 19 addresses the use of rehabilitation robots, socially interactive robots (SIR), and socially assistive robots (SAR), both virtual and embodied, to enhance, restore, or prevent early deterioration of cognitive abilities related to neurodegenerative disease or injury.
Integrating Robot Therapy With Neuro- and Psychophysiological Techniques
All the techniques and devices described until now use robot technology alone. Integration of different approaches and different technologies may improve the outcome, for instance, by training and restoring different functions within the same training session or by using physiological signals to monitor and/or control the recovery process. The following chapters focus on the use of neuro- and psychophysiological signals to enhance or complement robot-assisted therapy.
Chapter 20 presents hybrid FES-robot devices for training of activities of daily living aiming at the parallel restoration of functions by the external activation of paralyzed muscles and external mechanical support of postural functions. The combination of two modalities within the same treatment may multiply their individual effects as the external activation of muscles eliminates the need for large mechanical actuators and reduces the number of degrees of freedom to a controllable domain and, on the other hand, robot guidance removes the need for prolonged, fatiguing stimulation of muscles.
In spite of the acknowledged importance of proprioception for motor control and neuromotor rehabilitation, no effective method for assessment and rehabilitation of proprioceptive deficits has emerged in clinical practice. While there are many clinical scales for assessing proprioception, they all have insufficient psychometric properties and cannot be used in closed-loop treatment paradigms wherein treatment parameters are monitored and adjusted online or with a trial-by-trial frequency. Chapter 21 discusses how robots can simultaneously address two interrelated needs: to provide sensitive and repeatable assessments of proprioceptive integrity and to automate repetitive training procedures designed to enhance proprioception and its contributions to functional movement.
The outcome of a training program can be conditioned not only by the patient’s physical conditions but also by his/her psychophysiological state during the whole course of the rehabilitation program. Chapter 22 reviews psychophysiological response modalities that, together with task performance parameters and biomechanical measurements, may be used in a biocooperative approach to rehabilitation. The chapter focuses in particular on electrocardiogram, skin conductance, respiration signal, and peripheral skin temperature. Each signal is described in terms of acquisition modalities, signal processing, and features extraction. The psychophysiological responses in the case of multimodal challenge and physical activity are also examined, with reference to the differentiation of arousal and valence.
Understanding the mechanisms underlying muscle coordination during daily motor activities is a fascinating challenge in neuroscience and may provide important information pertaining to the recovery strategies of the neuromuscular system. Muscle synergies have been hypothesized as a neural strategy to simplify the control of the redundant motor actuators leading human movement and as a method to study motor coordination in healthy and neurological subjects. Chapter 23 presents the theoretical framework for the extraction and the description of muscle synergies. Moreover, it summarizes how neuropathologies impact on muscle synergies and their potential for neurorehabilitation. Finally, it discusses how muscle synergies can be used to assess the effectiveness of robot-aided rehabilitation and the design of innovative control strategies.
Robots and Information Technologies Advances Toward Long-Term Intervention
As the world’s population ages, the management of chronic diseases will become more important. This shift will put pressure on health-care systems that often focus on providing effective care while reducing costs. The use of technological advancements to augment health-care services provides a method to meet these demands. Telerehabilitation robotics, addressed in Chapter 24, combines established features of robot-assisted rehabilitation and tele-health care to provide distance rehabilitation services. While there is a growing market of robotic devices used in traditional rehabilitation settings, home-based implementations provide a unique set of challenges (e.g., remote monitoring, deployment constraints, and data management) that has limited the number of successful solutions. Clinical and kinematic outcomes show promising results and support further investigation. Cost analyses have demonstrated that telerehabilitation robotics is a cost-effective alternative compared with clinic-based therapy. While telerehabilitation robotics is a promising addition to conventional care, numerous barriers that limit practical integration will need to be addressed to allow a more widespread acceptance and use of this approach in rehabilitation.
As a final remark, robot rehabilitation involving an interactive high-intensity, intention-driven therapy based on motor learning principles and assist-as-needed leads to better outcomes than usual care in acute/subacute, chronic stroke and other pathologies. For this reason, clinical guidelines recommend the application of these technologies for the recovery of the lost functions.
This book highlights the most important technical aspects and strategies for the design, development, and application of robot technologies for rehabilitation purposes. With their ability to adapt exercise parameters based on physiological signals, objective and sensitive metrics reflecting the state and performance of a patient, the unique possibility to combine motor and somatosensory training, and the perspective of simple and wearable tools for home rehabilitation, robot devices promise further potential for the rehabilitation of neurological patients aiming at an improved motor function, a reduction of their disability, and overall an improved quality of life.
Businesses are finding more uses for Virtual Reality (VR) as the technology develops.
VR is no longer only for gaming or enjoyment. An American company called Blue Goji is using VR to improve one’s health by making exercise more fun.
Blue Goji has offices in Austin, the capital of Texas. The company demonstrated its cardiovascular workout machine, called the Infinity treadmill, at the recent South by Southwest festival. The event is held every year in Austin.
A person using the treadmill wears a virtual reality headset when exercising. Before starting, the user is connected to a belt to prevent falls. Then, the user plays a VR game while running on the machine. The game can transport the user into the virtual world, where he or she can be racing against virtual people.
The cost of the hardware and computer software program is $12,000. That is a lot of money for most people. But Kyra Constam of Blue Goji says the virtual reality treadmill is ideal for places where people go to exercise, like a high-end gymnasium or recreation center. She added that people seeking treatment at physical therapy or rehabilitation centers would find the equipment useful.
Recently, Leonardo Mattiazzi tested the Infinity treadmill. Mattiazzi said he had a strong feeling to actually get running and do something that pushed his limits. He said the experience was more interesting than running inside the gym without actually going anywhere.
Motion sickness less likely
Constam said the active use of virtual reality helps solve a common problem while wearing a VR headset. She noted that a lot of VR experiences cause motion sickness because people are in motion during the game, but not moving in real life. But when the user is moving on the treadmill and in the game, the chances of motion sickness are reduced, she said.
However, users who tested the treadmill while wearing the VR headset each had a different experience. It took Leonardo Mattiazzi 10 seconds to set the controls to running in the virtual world.
VR learning curve
Kyra Constam said there generally is a learning curve for VR. The first time users feel lost, but “the more you do it, the more you get used to it,” she said.
Mark Sackler was a first time user. He said he felt a little sick at one point during the game. But he thought the experience was surprisingly realistic.
After carefully studying the users’ experiences, Blue Goji plans to begin selling the Infinity treadmill to the public in 2019.
VOA’s Elizabeth Lee reported on this story from Texas. Xiaotong Zhou adapted her report for Learning English. George Grow was the editor.
A technique based on genetic bar codes can easily map the connections of individual brain cells in unprecedented numbers. Unexpected complexity in the visual system is only the first secret it has revealed.
A new technology for tracing the precise pathways of neural connections in the brain works with numbers of cells that were unimaginable until recently.
Sitting at the desk in his lower-campus office at Cold Spring Harbor Laboratory, the neuroscientist Tony Zador turned his computer monitor toward me to show off a complicated matrix-style graph. Imagine something that looks like a spreadsheet but instead of numbers it’s filled with colors of varying hues and gradations. Casually, he said: “When I tell people I figured out the connectivity of tens of thousands of neurons and show them this, they just go ‘huh?’ But when I show this to people …” He clicked a button onscreen and a transparent 3-D model of the brain popped up, spinning on its axis, filled with nodes and lines too numerous to count. “They go ‘What the _____!’”
What Zador showed me was a map of 50,000 neurons in the cerebral cortex of a mouse. It indicated where the cell bodies of every neuron sat and where they sent their long axon branches. A neural map of this size and detail has never been made before. Forgoing the traditional method of brain mapping that involves marking neurons with fluorescence, Zador had taken an unusual approach that drew on the long tradition of molecular biology research at Cold Spring Harbor, on Long Island. He used bits of genomic information to imbue a unique RNA sequence or “bar code” into each individual neuron. He then dissected the brain into cubes like a sheet cake and fed the pieces into a DNA sequencer. The result: a 3-D rendering of 50,000 neurons in the mouse cortex (with as many more to be added soon) mapped with single cell resolution.
This work, Zador’s magnum opus, is still being refined for publication. But in a paper recently published byNature, he and his colleagues showed that the technique, called MAPseq (Multiplexed Analysis of Projections by Sequencing), can be used to find new cell types and projection patterns never before observed. The paper also demonstrated that this new high-throughput mapping method is strongly competitive in accuracy with the fluorescent technique, which is the current gold standard but works best with small numbers of neurons.
Tony Zador, a neurophysiologist at Cold Spring Harbor Laboratory, realized that genome sequencing techniques could scale up to tame the astronomical numbers of neurons and interconnections in the brain.
The project was born from Zador’s frustration during his “day job” as a neurophysiologist, as he wryly referred to it. He studies auditory decision-making in rodents: how their brain hears sounds, processes the audio information and determines a behavioral output or action. Electrophysiological recordings and the other traditional tools for addressing such questions left the mathematically inclined scientist unsatisfied. The problem, according to Zador, is that we don’t understand enough about the circuitry of the neurons, which is the reason he pursues his “second job” creating tools for imaging the brain.
The current state of the art for brain mapping is embodied by the Allen Brain Atlas, which was compiled from work in many laboratories over several years at a cost upward of $25 million. The Allen Atlas is what’s known as a bulk connectivity atlas because it traces known subpopulations of neurons and their projections as groups. It has been highly useful for researchers, but it cannot distinguish subtle differences within the groups or neuron subpopulations.
If we ever want to know how a mouse hears a high-pitched trill, processes that the sound means a refreshing drink reward is available and lays down new memories to recall the treat later, we will need to start with a map or wiring diagram for the brain. In Zador’s view, lack of knowledge about that kind of neural circuitry is partly to blame for why more progress has not been made in the treatment of psychiatric disorders, and why artificial intelligence is still not all that intelligent.
Justus Kebschull, a Stanford University neuroscientist, an author of the new Nature paper and a former graduate student in Zador’s lab, remarked that doing neuroscience without knowing about the circuitry is like “trying to understand how a computer works by looking at it from the outside, sticking an electrode in and probing what we can find. … Without ever knowing the hard drive is connected to the processor and the USB pod provides input to the whole system, it’s difficult to understand what’s happening.”
Inspiration for MAPseq struck Zador when he learned of another brain mapping technique called Brainbow. Hailing from the lab of Jeff Lichtman at Harvard University, this method was remarkable in that it genetically labeled up to 200 individual neurons simultaneously using different combinations of fluorescent dyes. The results were a tantalizing, multicolored tableau of neon-colored neurons that displayed, in detail, the complex intermingling of axons and neuron cell bodies. The groundbreaking work gave hope that mapping the connectome — the complete plan of neural connections in the brain — was soon to be a reality. Unfortunately, a limitation of the technique in practice is that through a microscope, experimenters could resolve only about five to 10 distinct colors, which was not enough to penetrate the tangle of neurons in the cortex and map many neurons at once.
That’s when the lightbulb went on in Zador’s head. He realized that the challenge of the connectome’s huge complexity might be tamed if researchers could harness the increasing speed and dwindling costs of high-throughput genomic sequencing techniques. “It’s what mathematicians call reducing it to a previously solved problem,” he explained.
Video: Tony Zador explains the new MAPseq technology and its potential for unlocking the secrets hidden in details of the brain’s connectivity.
In MAPseq, researchers inject an animal with genetically modified viruses that carry a variety of known RNA sequences, or “bar codes.” For a week or more, the viruses multiply inside the animal, filling each neuron with some distinctive combination of those bar codes. When the researchers then cut the brain into sections, the RNA bar codes can help them track individual neurons from slide to slide.
Zador’s insight led to the new Nature paper, in which his lab and a team at University College London led by the neuroscientist Thomas Mrsic-Flogel used MAPseq to trace the projections of almost 600 neurons in the mouse visual system. (Editor’s note: Zador and Mrsic-Flogel both receive funding from the Simons Foundation, which publishes Quanta.)
Six hundred neurons is a modest start compared with the tens of millions in the brain of a mouse. But it was ample for the specific purpose the researchers had in mind: They were looking to discern whether there is a structure to the brain’s wiring pattern that might be informative about its function. A currently popular theory is that in the visual cortex, an individual neuron gathers a specific bit of information from the eye — about the edge of an object in the field of view, or a type of movement or spatial orientation, for example. The neuron then sends a signal to a single corresponding area in the brain that specializes in processing that type of information.
These images offer an example of how MAPseq can determine the wiring of multitudes of neurons. The small colored dots in the first image represent the positions of the cell bodies of 50,000 neurons in the cortex of a mouse. In the second image, the axon projections from just two of those neurons to endpoints elsewhere in the brain are shown. In the third image, the pathways from many more of the neurons are superimposed.
To test this theory, the team first mapped a handful of neurons in mice in the traditional way by inserting a genetically encoded fluorescent dye into the individual cells. Then, with a microscope, they traced how the cells stretched from the primary visual cortex (the brain area that receives input from the eyes) to their endpoints elsewhere in the brain. They found that the neurons’ axons branched out and sent information to many areas simultaneously, overturning the one-to-one mapping theory.
Next, they asked if there were any patterns to these projections. They used MAPseq to trace the projections of 591 neurons as they branched out and innervated multiple targets. What the team observed was that the distribution of axons was structured: Some neurons always sent axons to areas A, B and C but never to D and E, for example.
These results suggest the visual system contains a dizzying level of cross-connectivity and that the pattern of those connections is more complicated than a one-to-one mapping. “Higher visual areas don’t just get information that is specifically tailored to them,” Kebschull said. Instead, they share many of the same inputs, “so their computations might be tied to each other.”
Nevertheless, the fact that certain cells do project to specific areas also means that within the visual cortex there are specialized cells that have not yet been identified. Kebschull said this map is like a blueprint that will enable later researchers to understand what these cells are doing. “MAPseq allows you to map out the hardware. … Once we know the hardware we can start to look at the software, or how the computations happen,” he said.
MAPseq’s competitive edge in speed and cost for such investigations is considerable: According to Zador, the technique should be able to scale up to handle 100,000 neurons within a week or two for only $10,000 — far faster than traditional mapping would be, at a fraction of the cost.
Such advantages will make it more feasible to map and compare the neural pathways of large numbers of brains. Studies of conditions such as schizophreniaand autism that are thought to arise from differences in brain wiring have often frustrated researchers because the available tools don’t capture enough details of the neural interconnections. It’s conceivable that researchers will be able to map mouse models of these conditions and compare them with more typical brains, sparking new rounds of research. “A lot of psychiatric disorders are caused by problems at the circuit level,” said Hongkui Zeng, executive director of the structured science division at the Allen Institute for Brain Science. “Connectivity information will tell you where to look.”
High-throughput mapping also allows scientists to gather lots of neurological data and look for patterns that reflect general principles of how the brain works. “What Tony is doing is looking at the brain in an unbiased way,” said Sreekanth Chalasani, a molecular neurobiologist at the Salk Institute. “Just as the human genome map has provided a scaffolding to test hypotheses and look for patterns in [gene] sequence and function, Tony’s method could do the same” for brain architecture.
The detailed map of the human genome didn’t immediately explain all the mysteries of how biology works, but it did provide a biomolecular parts list and open the way for a flood of transformative research. Similarly, in its present state of development, MAPseq cannot provide any information about the function or location of the cells it is tagging or show which cells are talking to one another. Yet Zador plans to add this functionality soon. He is also collaborating with scientists studying various parts the brain, such as the neural circuits that underlie fear conditioning.
“I think there are insights to be derived from connectivity. But just like genomes themselves aren’t interesting, it’s what they enable that is transformative. And that’s why I’m excited,” Zador said. “I’m hopeful it’s going to provide the scaffolding for the next generation of work in the field.”
Does progressive resistance training improve strength and activity after stroke? Does any increase in strength carry over to activity?
Systematic review of randomised trials with meta-analysis.
Adults who have had a stroke.
Progressive resistance training compared with no intervention or placebo.
The primary outcome was change in strength. This measurement had to be of maximum voluntary force production and performed in muscles congruent with the muscles trained in the intervention. The secondary outcome was change in activity. This measurement had to be a direct measure of performance that produced continuous or ordinal data, or with scales that produced ordinal data.
Eleven studies involving 370 participants were included in this systematic review. The overall effect of progressive resistance training on strength was examined by pooling change scores from six studies with a mean PEDro score of 5.8, representing medium quality. The effect size of progressive resistance training on strength was 0.98 (95% CI 0.67 to 1.29, I2 = 0%). The overall effect of progressive resistance training on activity was examined by pooling change scores from the same six studies. The effect size of progressive resistance training on activity was 0.42 (95% CI –0.08 to 0.91, I2 = 54%).
After stroke, progressive resistance training has a large effect on strength compared with no intervention or placebo. There is uncertainty about whether these large increases in strength carry over to improvements in activity.
In this blog for stroke survivors, their families and clinical staff, Mark Smith, Consultant Physiotherapist in Stroke Rehabilitation, looks at Cochrane evidence on physical rehabilitation approaches for the recovery of function and mobility following stroke and explores the importance of the findings with respect to service delivery in an ever changing landscape of health and social care.
Stroke is often termed a “recovering neurological condition”, but how much recovery can we expect in response to what sorts of intervention and in what doses? Strokes happen as a result of a disturbance of the blood supply to the brain, mostly in older people and mostly due to the blocking of arteries supplying oxygenated blood to the brain. But less commonly, strokes can affect younger people (and children) and may also be the result of a burst blood vessel causing a haematoma (collection of blood) within the brain mass.
The stroke “pathway” extends from the initial hyper-acute episode, usually the first minutes and hours post onset of symptoms in the community setting, emphasising that “time is brain” through the “FAST” (Face, Arm, Speech, Time to call 999) campaign supported by the main UK stroke charities, Stroke Association and Chest, Heart & Stroke Scotland. The aim is to deliver patients with a suspected stroke to appropriately specialist hospital/stroke unit care as soon as possible in order to receive life saving and disability reducing hyper-acute interventions such as thrombolysis (using clot-busting drugs) and more recently thrombectomy (breaking and removing the clot with tools) in patients who meet the necessary criteria.
This hyper-acute stage of the pathway is highly evidence-based, medicalised and thoroughly audited across the UK by the two main stroke audits – Sentinel Stroke National Audit Programme (SNNAP) in England and Wales, and the Scottish Stroke Care Audit (SSCA) in Scotland, with a view to time critical delivery to all eligible patients. However, the subsequent audit around rehabilitation interventions can be less thorough despite a growing body of evidence to support physical interventions.
Ongoing physical rehabilitation: what should we do?
Most patients with stroke will need some kind of ongoing physical rehabilitation to assist them in achieving best outcomes possible (with respect to the severity of the stroke but also with respect to the resource available) and we are increasingly becoming aware that there are some critical elements in achieving that. But can we, and do we, deliver what patients should receive in our publicly funded UK health and social care system and is the evidence sufficiently persuasive to argue strongly for this? How do we ensure that a health condition such as stroke which spans a pathway from the community through hyper-acute medical hospital care, possibly downstream in-patient rehabilitation and back to the community via health and social care is fit for purpose? And how do we remove the diagnostic stroke “badge” and simply allow an individual to function again in society with the support they need to manage their long term condition?
Perhaps there is a persuasive argument for delivering evidence-based stroke rehabilitation with appropriate levels of quality and intensity as it is considered a human right in many societies?
The evidence for physical rehabilitation after stroke
Evidence for physical interventions relating to walking and physical rehabilitation after stroke is becoming increasingly available in the form of high quality systematic reviews that can inform clinical guidelines as well as high level government strategy with respect to stroke. We tend to find it mostly relating to physical therapy and exercise/fitness interventions.
Updating Cochrane evidence: a novel approach
Pollock et al (2014a) revisited an older Cochrane Review (Pollock et al 2009). Previous versions of the review had focussed on physiotherapy interventions for the lower limb and walking after stroke but they decided to use a novel approach in the reappraisal of the literature and update of the evidence. The review was subsequently re-titled Physical Rehabilitation Approaches for the Recovery of Function, Balance and Walking following Stroke. The academic elements of reviewing papers followed the usual Cochrane protocol.
Seeking “real world” views on the evidence
In order to gauge the relevance of the evidence for clinical practice, but also critically for stroke survivors and carers, in parallel with revisiting the evidence through systematic review,co Pollock and colleagues also convened a multi key stakeholder short life working group comprised of stroke survivors, carers and clinical staff. This group was charged with sense-checking and “validating” the evidence as being clinically relevant as it emerged, using formal group consensus methods based on nominal group techniques. This involved a system of voting which focussed the group in reaching consensus. The academic researchers involved in the systematic review attended the working group meetings and presented the various options in directing the review, but did not vote themselves so as to minimise bias. This arm of the project culminated in a presentation at the 2014 Cochrane UK and Ireland Symposium, held in Manchester, in which key stakeholders in the review led a workshop on user-involvement in writing Cochrane Reviews. The dual aims of this work were to determine if physical treatment approaches are effective in the recovery of function and mobility in patients with stroke and to see whether any one physical treatment approach is more effective than any other approach.
The presentation of the updated evidence as a result
Ninety six studies, involving 10,401 stroke were included in the review (Pollock et al 2014a). Results of 27 studies (3243 stroke survivors) could be combined comparing physical rehabilitation with no treatment at all. Twenty five of these studies were carried out in China and were unusual in that they compared an active treatment/intervention group to a control group with no clinical intervention. Additional physical rehabilitation versus usual care was described in 12 of these studies demonstrating improved motor function (887 stroke survivors), standing balance (five studies, 246 stroke survivors) and walking speed (14 studies, 1126 stroke survivors). There was also limited evidence of dose intensity for the first time, with treatment durations given between 30 and 60 minutes per day apparently carrying the most significant benefits, but future research needs to verify this.
Physiotherapy, using a mix of components from different approaches, is effective for the recovery of function and mobility after stroke. Treatment sessions of 30-60 minutes, 5-7 days a week may provide a significant beneficial effect.
No one approach to physical treatment is any more (or less) effective in promoting recovery of function and mobility after stroke.
Physiotherapists should use their expert clinical reasoning to select individualised, patient-centred, evidence-based physical treatment, with consideration of all available treatment components, and should not limit their practice to a single “named” approach.
Fitness training after stroke
This work is supported by another recently updated Cochrane Review around Fitness Training for Stroke Survivors (Saunders et al 2016) which included 58 trials involving 2797 participants with stroke. These studies were grouped according to the type of fitness training intervention – cardiorespiratory (28 trials, 1408 participants) resistance (13 trials, 432 participants) and mixed training (17 trials, 4342 participants).
Cardiovascular fitness training, particularly involving walking, can improve exercise ability and walking after stroke.
Mixed training improves walking ability and improves balance.
Unable to draw reliable conclusions regarding effects on quality of life, mood or cognitive function.
No evidence of injury or other health problems and exercise appears to be a safe intervention.
Circuit Class Therapy
English et al (2017) included 17 trials involving 1297 stroke survivors (most of whom could walk 10 metres) in another recent Cochrane Rcoeview to examine the effectiveness and safety of Circuit Class Therapy (CCT) on mobility in adults with stroke. Ten studies (835 participants) measured walking capacity, demonstrating that CCT was superior to the comparison intervention, eight measured gait speed again finding that CCT was of significant benefit. Their conclusion was that there was moderate evidence to suggest that CCT is effective in improving mobility for people after stroke. These effects may be greater later after the stroke and stroke survivors may be able to walk further, faster, with more independence and confidence in their balance, but further high quality research is required.
Other relevant reviews
There have also been Cochrane Reviews providing low to moderate quality evidence of the rehabilitation benefits of electro mechanically or robotic assisted gait training devices (Mehrholz et al 2017a), treadmill training for stroke patients who could already walk (Mehrholz et al 2017b) and repetitive task training (French et al 2016). A Cochrane overview (a review of systematic reviews) presenting moderate quality evidence for upper limb rehabilitation after stroke, suggested beneficial effects of constraint-induced movement therapy (CIMT), mental practice, mirror therapy, interventions for sensory impairment, virtual reality and a relatively high dose of repetitive task practice (Pollock et a. 2014b). Again, information was insufficient to reveal the relative effectiveness of different interventions.
Well, the research evidence, albeit largely of moderate quality, points to the efficacy of a broad range of interventions in the physical rehabilitation of people with stroke, with little detail about which specific interventions are of most value in which settings, and indeed the best delivery mechanisms to make them most easily and effectively implemented. More research is needed to generate higher quality evidence and implementation guidance. Recommendations in stroke guidelines (RCP 2016) and stroke strategies (Scottish Government 2014) have been made on the basis of these findings, particularly with respect to adequate dose. However, given that studies are disparate, have been derived from around the world and as a result conducted within a great variety of different healthcare (and social care/leisure) settings, it is challenging for clinicians to know exactly how to implement the reported findings.
The work of Pollock et al (2014) in engaging multi key stakeholders in making more “real” the findings of their systematic review made an effort to think about how we might implement the evidence, particularly in relation to the views of stroke survivors, carers and therapists. Perhaps we need to be less defensive of historical professional and service silo boundaries and use this evidence in the best interests of the stroke survivors we aim to serve, though imaginative use of commissioning mechanisms, third sector organisations, the leisure industry, healthcare staff resources and the capacity we have to deliver stroke rehabilitation interventions?
The World Health Organisation (WHO) has recently argued that the benefits of rehabilitation are realised beyond the health sector and that delivered appropriately can reduce care costs and enable participation in education and gainful employment (WHO 2017). With respect to the stroke pathway, if we are serious about saving lives at the “front door”, let’s also make them worth living at the “back door” and beyond.
Join in the conversation on Twitter with @CochraneUK #LifeAfterStroke or leave a comment on the blog.
Physicians, trainees and even laypeople can now stand right beside an expert radiologist as he performs one of the most difficult medical procedures of its kind – in virtual reality.
Ziv Haskal, MD, of the University of Virginia Health System, has created a dramatic teaching tool using the power of virtual reality. Whether watched on a high-end VR system or an inexpensive cardboard viewer, Haskal’s virtual procedure puts the viewer right next to him as he creates a new blood vessel in a patient’s liver through a small nick in the patient’s neck.
It’s a complicated procedure – Haskal calls it an “interventional radiology heptathlon” – and his use of VR is set to transform how it is taught. “The current means of teaching is a physical person has to arrive … and go over with the doc beforehand. Or they have to look at a lousy 2D animation on a screen,” Haskal said. “Once you put [VR] glasses on people, it’s like you walk them through a completely different door.”
IR in VR
From inside the VR goggles, viewers can look around in 360 degrees as the procedure, known as a transjugular intrahepatic portosystemic shunt, unfolds around them. Haskal guides them step-by-step through the entire procedure, and strategic use of picture-in-picture lets the viewer see both what Haskal is doing and what he is seeing.
Haskal designed the VR experience as a teaching tool for physicians and trainees, but he can foresee many other game-changing applications. VR might be used to show a patient what to expect during a procedure, to teach a nursing student what must be kept sterile in an operating room or to provide a refresher for physicians who have not performed the procedure recently.
“Watching it in a 2D animation, listening to a lecture, watching a physician on a video simply fails to convey the subtleties of the procedure,” Haskal said. “We’re putting the viewer in the actual environment, where they can return again and again.”
Lifting the Curtain
Haskal debuted the VR tool last weekend at the SIR 2018 Scientific Meeting in Los Angeles. He plans to make the VR publicly available to everyone, for free, on the Journal of Vascular and Interventional Radiology website. (Video clips from the VR video can’t do it justice, but to get a sneak peak at what it’s like, visit UVA’s Making of Medicine blog at https://makingofmedicine.virginia.edu/2018/03/13/into-the-or-in-vr/ )
Ultimately, Haskal hopes to create many more virtual-reality teaching tools for healthcare professionals. “With this approach,” he said, “doctors are simply going to be able to do things better.”
Deep in the hippocampus, are new neurons born throughout life? Just when scientists were about to reach some consensus that the answer was yes, two recent studies disagree. In the April 5 Cell Stem Cell, Maura Boldrini and colleagues at Columbia University, New York, report that adult neurogenesis not only exists, but remains steady into old age. The researchers counted newborn neurons in samples from people aged 14–79 years, and came up with similar numbers. In the March 7 Nature, researchers led by Arturo Alvarez-Buylla at the University of California, San Francisco, reported that while neural progenitors abounded in postmortem hippocampi from prenatal or early childhood brains, they fell off the map by age 7. What gives? In older people, some of the cells that expressed markers of budding neurons turned out to be glia, the authors claim.
Neural progenitor cells proliferate in human hippocampus throughout adulthood, says a new study.
It blames waning angiogenesis, not faulty neurogenesis, for lost neuroplasticity in old age.
In contrast, another paper claims brain neurogenesis fizzles during childhood.
It claims some cells bearing neural progenitor markers are actually glia.
Who is right? Researchers who spoke with Alzforum stood squarely behind Boldrini because she used stereology, a gold standard quantitation method, to estimate numbers of neural progenitors throughout the entire dentate gyrus of postmortem brain. Alvarez-Buylla’s team estimated cell numbers using only three to five slices from each postmortem sample. “It would be very difficult to rule out neurogenesis by this method,” said Orly Lazarov of the University of Illinois in Chicago, who pointed out that because the study relied on small numbers of individuals per age group, the data could be misleading.
Others, including Jonas Frisén of the Karolinska Institute in Stockholm, pointed to a long list of previous papers supporting the existence of neurogenesis in the adult human brain. “An analogy is that 10 people go into the woods to search for blueberries,” he wrote, “Nine come back with blueberries and one does not. Are there blueberries in that forest?”
Still, others acknowledged that some of those “blueberries” might have been glia. “To me, it boils down to a single question: are these proliferating cells truly neural progenitors, or not?” asked Costantino Iadecola of Weill Cornell Medical College in New York.
Numerous studies in rodents support the idea that neural progenitors in the mammalian brain continue to trickle out fresh neurons into adulthood, though factors such as aging and disease dampen the flow (Altman and Das, 1965; Sep 2001 news; Feb 2002 news; Kempermann et al., 2003; Mar 2010 news). Tracking neurogenesis in humans has been trickier, but a seminal study two decades ago set the stage: Five terminal cancer patients received an injection of bromodeoxyuridine (BrdU), a dye that incorporates into DNA during cell division. Postmortem analyses revealed evidence of dividing neurons in the dentate gyri of all of the patients (Eriksson et al., 1998). Since then, carbon-14 tracing and immunohistochemistry studies have supported the idea that new neurons arise in the adult human brain (Knoth et al., 2010; Jun 2013 news; Feb 2014 news).
Boldrini and colleagues set out to determine if age affects adult neurogenesis. They acquired hippocampal samples from 11 women and 17 men, aged 14–79, who were cognitively normal, had suffered no brain trauma, had had no microvascular pathology in the brain, and had clean toxicology reports at the time of death. The researchers collected 50-micron thick sections every 2 mm along the entirety of the hippocampus, and used both immunofluorescence and immunocytochemistry to label various cell-surface markers associated with five different stages of neural development (see image below). Finally, they estimated cell numbers throughout the dentate gyrus using stereology, whereby a computer algorithm calculates total cell numbers in a region by combining data from multiple sections. They reported the number of neural progenitors in the anterior, mid-, and posterior dentate gyrus.
The earliest neural progenitors known, called quiescent radial-like type I neural progenitors (QNPs), express GFAP, a marker shared with astrocytes; nestin, an intermediate filament protein that marks neural stem cells; and the transcription factor Sox2, which is required for the maintenance of multipotent stem cells. The researchers found that numbers of these cells decreased with age in the anterior-mid dentate gyrus. This is in keeping with the prevailing view that people are born with a finite number of these QNPs, Boldrini said.
QNPs give rise to type II intermediate neural progenitors. INPs are proliferating cells that express Ki67, a marker of actively dividing cells. Neuroblasts, or type III INPs, also proliferate, but lose expression of GFAP and Sox2. Based on expression of Ki67, nestin, and Sox2, the researchers determined that numbers of type II and III INPs remained steady, on the order of thousands of cells, in all three regions of the dentate gyrus throughout life. These neural progenitors were found in the subgranular zone (SGZ), which is proposed to be the predominant neurogenic niche in the region, as well as the granule cell layer (GCL, see image below). The findings pointed to a stable supply of neural progenitors in the dentate gyrus throughout adult life.
The researchers next asked whether those progenitors would fulfill their destiny and give rise to immature neurons and, ultimately, bona fide granule neurons. On the way to becoming fully fledged neurons, type III INPs start to express doublecortin (DCX), a microtubule-associated protein involved in neural migration. They also produce polysialylated neural cell adhesion molecule (PSA-NCAM), a glycoprotein they need for plasticity. Together, DCX and PSA-NCAM mark young neurons, which continue to express both proteins until they differentiate into mature neurons, whereupon they suppress DCX. The researchers found that the tissue donors had similar numbers of cells co-expressing DCX and PSA-NCAM, regardless of their age, suggesting neurogenesis continued unabated throughout life. Numbers of NeuN+ mature neurons also held steady, indicating that neuronal loss in the dentate gyrus is not a characteristic of healthy aging, either.
The researchers calculated that each dentate gyrus had between 10,000 and 15,000 young neurons (i.e., type III INPs and immature neurons). While the functional significance of these cell numbers is unclear, Boldrini speculated that this ongoing level of neurogenesis influences neural circuitry and cognition. For this reason, boosting neurogenesis could be a therapeutic strategy for neurodegenerative disease, she said.
However, while older adults appear to generate as many new neurons as younger people, those new cells may be less plastic, judging by a decline in PSA-NCAM+/DCX– cells in the anterior dentate gyrus. Curiously, using endothelial markers and stereology to measure the numbers, length, bifurcations, and total volume of capillaries, the scientists also found an age-dependent decline in angiogenesis in the same regions. The researchers proposed that a decline in angiogenesis may trigger loss of neuroplasticity without necessarily affecting neurogenesis, for example by starving new neurons of essential growth factors or nutrients.
Others were not convinced, noting that reliance on a single marker—PSA-NCAM—made the plasticity results no more than an interesting correlation. Still, Lazarov and Iadecola said the connection between age-related decline in angiogenesis and neuroplasticity was plausible. Iadecola was surprised that loss of angiogenesis did not appear to affect neurogenesis, but he noted that the donors had no obvious vascular pathology in their brains. Perhaps in people with more severe vascular problems, neurogenesis would be affected, he said.
In Grown-Up Brain, Nary a Newborn Neuron
In the Nature paper, first author Shawn Sorrells and colleagues used many of the same markers—Sox2, GFAP, DCX, and PSA-NCAM—to assess neurogenesis in postmortem samples across the lifespan. This included 11 samples from prenatal donors, the youngest of whom was only at 14 weeks gestation. They also analyzed seven samples from infants who died during their first year of life, one from a 7-year-old, one from a 13-year-old, and 17 samples from adults up to 77 years of age at the time of death. The samples came from multiple sources, and were not limited to healthy donors, or all postmortem. They included hippocampal tissue from surgical resection in 22 people with epilepsy, who ranged from three months to 64 years old.
For the postmortem samples, the researchers used three to five coronal sections to assess cell numbers. Rather than using stereology to estimate the total number of cells in the dentate gyrus, the researchers counted cells in individual sections. Three researchers independently counted each section while blinded to the age of the donor. They identified key structural landmarks, most notably the cell-dense GCL, to infer the relative locations of the cells.
In prenatal samples, the scientists found abundant proliferating Ki67+ cells that expressed the progenitor markers Sox1 and Sox2. Numbers of these cells plummeted during the first year of life, and were near zero in samples from people 7 or older. Notably, these proliferating cells never coalesced beneath the GCL to form a distinctive layer in the SGZ, a structural niche that supports neurogenesis in rodent models. The researchers confirmed the absence of this layer by electron microscopy on a subset of their samples, ranging in age from 22 gestational weeks to 48 years of age.
DCX+/PSA-NCAM+ cells, representing intermediate neural progenitors and immature neurons, clustered throughout the GCL at birth to a density of about 1,600 cells per mm2. In prenatal and infant samples, these cells had a smooth, elongated morphology characteristic of young neurons. By 13 years of age, sections only contained around two young neurons per mm2, or roughly one or two cells per section. Likewise, the investigators found no evidence of young neurons in samples from epilepsy patients older than 11. As for adults, none of the surgical or postmortem samples contained DCX+/PSA-NCAM+ cells, however the researchers did find cells that expressed PSA-NCAM without DCX. Unlike the elongated young neurons in infant samples, these cells had a more mature neuronal morphology with distinct axons and dendrites, and expressed NeuN, suggesting they were highly plastic neurons. The researchers also identified DCX+ cells in some older childhood and adult samples, but these cells co-expressed glial markers, and under the gaze of electron microscopy, had glial morphology.
The researchers also looked for evidence of neurogenesis in rhesus macaques. By staining with similar neuronal markers, they found that unlike in the human brain, proliferating neural progenitors did gather in the SGZ before birth. However, the number of these young neurons decreased eightfold between birth and 1.5 years of age, and were sparse in 7-year-old animals. Similarly, labeling dividing cells with BrdU revealed a steep drop-off in dividing neurons between 1.5 and 7 years of age.
The researchers concluded that neurogenesis is robust only in the earliest stages of development, and that DCX+ cells in late childhood and adult samples were actually glia. In an email to Alzforum, Sorrells and Alvarez-Buylla speculated that the cells identified as young neurons in the Boldrini study were also likely non-neuronal. “Identifying new neurons is technically challenging—in our own recent study we made similar observations to what Boldrini et al. report, but after extensive additional analysis of the shape and appearance of the cells in question, including electron microscopy and gene expression, we determined that these cells were not in fact young neurons or neural progenitors but different types of cells altogether,” they wrote.
However, Boldrini asserted that in her study, the cells stained for both DCX and PSA-NCAM did not co-localize with cells that appeared to be glia based on the pattern of Nissl staining, and were present in the thousands. Boldrini added that the immature neurons took on a pyramidal shape, characteristic of neurons, not glia.
Sorrells and Alvarez-Buylla further drew attention to the lack of a defined layer of proliferating cells in the SGZ in their study, adding that Boldrini’s samples also appeared to lack a distinct layer of cells there. In rodents, neural progenitors gather and proliferate in the SGZ. On this issue, Boldrini thinks that perhaps in humans neurogenesis occurs in a more scattered fashion. She said that for this reason, taking stock of cells throughout the entire dentate gyrus is crucial to capture these sparse cells.—Jessica Shugart
Researchers recently published an article in TheLancet Neurology discussing the difficulties facing seizure detection in patients with epilepsy.
Epilepsy is a neurological disorder that is characterised by short repetitive epileptic seizures. These seizures can be harmful to the individual depending on the circumstances in which they occur, such as a seizure while driving. This disorder is set apart from other neurological disorders since there is a broad range of different physiological changes that can cause it, leading to a large variation in symptoms and making it difficult to treat. While 70% of sufferers can be treated with pharmacological agents, 30% have no reliable anti-epileptic drugs that are effective for their particular type of epilepsy.
In a recent study, Christian Elger and Christian Hoppe determined that a key challenge facing patients is that over 50% of patients under-report the number of seizures they experience, which has a serious impact on how well doctors are able to determine what treatments are most suitable for them. This also calls into question many of the previously published research on epilepsy treatments. They recently published this report in The Lancet Neurology.
Why are Epileptic Seizures Difficult to Detect?
In this personal view, the writers determined that the cause of under-reporting is primarily due to patients, or their caregivers, being unable to identify when seizures are occurring. Seizures can impair consciousness, may occur at night, or the physical symptoms may be so subtle that they are not easily noticed unless professionally trained to do so.
Technologies for Epilepsy DetectionThe gold standard for epilepsy detection is video-electroencephalography (VEEG), where patients have their brain activity monitored for epilepsy-specific activity and trained technicians can test for impairments to consciousness, cognition, language, and memory. Video footage from the VEEG can also be viewed at a later time to spot slight body movements indicative of a seizure. The limitation of this method is that it requires a hospital visit, increasing associated costs, and is only suitable for identifying how frequent a person has a seizure over a given time, it does not address the issue of a person (or their caregiver) being aware they are having a seizure in real time.
Automated System Required
It is clear that the future of seizure detection requires an automated system, preferably one that patients can wear over the long-term and that will notify them or a nearby center when a seizure occurs. The main barriers to this technology is that a number of the current ambulatory systems for monitoring brain activity can be limited in monitoring time (72 hours) or require labor-intensive analysis of data, although as algorithms for analyzing brain activity improve this limitation will also decrease.
An analysis of movement via home-based video systems, or of various physical data outputs (i.e. accelerometry, magnetometry, gyroscopy, or pressure data) derived from worn sensors have some promise but so far the results have not been consistent.
It appears that one of the most promising methods, which is also viewed favorably by patients, is surface electromyography (SEMG). This method involves self-adhesive sensors that are attached to muscles in areas of the body affected by seizures. Furthermore, multi-modal approaches that combine SEMG, EEG, and electrocardiography have a detection rate over 85% for the majority of seizure types.
Improved Seizure Detection Necessary
It is clear that improved seizure detection is necessary for ensuring that doctors provide the most appropriate treatment to individual patients, as well as ensuring that patients are protected from life-threatening seizures. Improving wearable, ambulatory technologies and advancements in algorithms for the analysis of seizure data will help provide comprehensive support to both physicians and to the patients that they monitor.
Written by Michael Healy, BSc, MSc
Reference: Elger C.E., Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17: 279–88.
MIAMI, Fla. (Ivanhoe Newswire) — According to the Epilepsy Foundation, one in 26 Americans will develop the seizure disorder at some point in their lives. Now, new technology is decreasing the frequency of seizures, helping patients live healthier lives.
Mark Weinberg has trouble remembering the car accident that changed his life at 16.
“My parents say I was in a coma for four days,” Weinberg shared.
Weinberg survived but suffered a severe brain injury and started having seizures every week.
“I’ll go up to someone and say can you hold my hand, I think I’m having a seizure,” he said.
Andres Kanner, MD, Chief of the Epilepsy Division at the University of Miami Miller says a seizure is like a short circuit in the brain.
Dr. Kanner explained, “They can lose awareness of their surroundings and be unresponsive and they don’t know what’s happening around them.”
Dr. Kanner says medication can control seizures in 70 percent of patients. But for Mark that wasn’t the case.
Weinberg said, “I think I’ve been on almost every medicine.”
Now new technology is helping patients like Mark. It’s called responsive neuro stimulation or the RNS system.
“Imagine a pacemaker, which has a computer chip in it,” said Dr. Kanner.
The device made by Neuropace is implanted under the scalp and connected to the areas in the brain causing seizure activity.
Dr. Kanner continued, “As it detects that abnormal pattern it sends an electrical stimulation.”
That stimulation prevents the seizure from happening. Since having the device implanted, Weinberg’s seizures have been cut in half.
“Even if I do have them they’re shorter so I’m not as scared as I used to be,” Weinberg told Ivanhoe.
Now he’s going to college, living a life with fewer seizures.
Dr. Kanner says studies show the device is safe and does not affect cognitive function. Dr. Kanner says in the first year 30 to 40 percent of patients notice their seizure frequency may be cut in half.