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Learning Technology -attemt at tech ology and history

Learning Technology -attempt at tech ology and history

What distinguishes us as a species is tools and technology. More accurately, what distinguished the many species that we evolved from and co-evolved alongside us is technology. It is not that other species such as birds and primates, do not use tools, but the first of our genus Homo habilis (handyman) through to Homo sapiens (knowing man) are simply the most successful of these competing species. We had better tools and technology.

We see technology as a noun, not a discipline or subject. There is no -ology for technology, stuck as it is somewhere between science and engineering. Yet this is an area of human endeavor that has shaped history, economics, sociology, psychology, and philosophy. What we need is a focus on Technology, like Archaeology, Geology, Biology, or Sociology. We need a deeper understanding of technology in terms of its history and role in history.

To this end, we can look towards the science of technology as uncovering certain laws of technology, not laws as in physics, chemistry, or biology, but rules that emerge from the way we invent, develop, deliver and use learning technology.

Learning technology

But there is one type of technology that differs from the rest – learning technology. At key moments in our history, fundamental technologies were invented that unlocked massive cultural growth.

1. Learning technology has a cultural & economic impact

2. Learning technology is a multiplier

3. Learning technology extends cognition

4. Learning technology replaces teaching with learning

5. Learning technology scales

6. Learning technology is enabling new pedagogies

7. Learning technology draws from consumer technologies

8. Learning technology melds hardware and software

9. Learning technology can be both good and bad

10. Learning technology gets declassified

Learning technology has a cultural impact

There have been many attempts to read history as being catapulted forward in economic cycles caused by innovations in technology. What no one has ever done is apply that logic to learning technologies, yet technological advances may have led to specific advances in stone axe technology and cave images, that required the teaching and learning of sophisticated skills. Writing, a skill that had to be formally taught and learned, undoubtedly led to cultural and economic change, as did the alphabet, that tweak to writing that gave eastern Mediterranean culture a central role in early science, philosophy and literature, especially in Greece, a cultural root that reverberates down to us today. The world was in a steady state during the Dark Ages when writing technology was stuck in manuscript production, in the hands of a priestly class, but sprang into life with printing, causing a Scientific Revolution and Religious Reformation. The era of mass schooling and mass production of pencils, pens, paper, notebooks, chalkboards, literally educated the masses, increasing the cultural capital of the world. Then came calculators and computers that sped up technological progress. It got us to the moon and energized economies and the management of capital. Hot on its tail, as this process is exponential, came the internet, which globalized finance, economics, production and consumption. Finally, we are in the era of AI and data. We are already feeling the pressure in the age of algorithms. For the first time we have learning technology that doesn’t just help us tech and learn – it can learn itself. We are only just getting to grips with the idea that learning is not the sole domain of our genus but of the very technology we have created.

On the mechanics of technological change, a seminal text is Schumpeter's Theories of Economic Development, where cycles of economic development are seen as being driven by innovative technology as their cause. Carla Perez in Technological Revolutions and Financial Capital expands on the idea to identify specific cycles of over-effusive investment, slumps, then a period of fruitful investment that results in significant improvements in productivity. In other words, we overestimate technology in the short-term, underestimate it in the long-term. There is a parallel process with learning technologies. Technologies are invented, then go through a period of non-learning use, eventually being applied in teaching and learning. It is when a technology is used in teaching and learning that the multiplier effect works through to economic growth.

Learning technology is a multiplier

These learning technologies; language, writing, printing, computers, the internet and now AI, released exponential growth in knowledge and learning. The ability to learn, document, write, calculate, explain, plan and execute most of what we normally think of technology (mechanical, electrical, chemical and physical) relied on and still relies on learning technologies that underly their ideation. The great leaps forward in the history of our species have been on the back of these leaps of the imagination, the invention of technologies that allow us to use and document our imaginative output to further civilization. It has given rise to great art, science, advances in medicine, finance and every other human achievement.

Learning technology is a cultural and economic multiplier. It is not simply additive. It is the root cause for paradigm shifts. These shifts are often seen in terms of materials (stone, bronze, iron, water, steel, steam, silicon chips) or mechanical devices (weapons, ships, steam engines, looms, cars, domestic devices, airplanes, rockets, satellites, computers, tablets, smartphones), when what lies beneath their development is learning, enabled by learning technologies. The more obvious visible, physical manifestations, are in fact, the effects or products of that wider and deeper learning. Learning has been progressively accelerated by learning technologies and that has had a compounding effect on output. With each successive development of technology, from cave paintings, writing, alphabets, printing, computers, the internet, and AI, we get acceleration.

Yet learning technology is mostly ignored in favor of the crude, mechanical history. We think of technology in terms of one form of technology – the obvious and physical. We still fail to recognize that the history of technology has a hidden story of cognitive effort and progress that made this surface technology possible. Beneath the physical manifestation of technology lies cognitive technology that the brain itself uses to create and share ideas – learning technology. “At a few times in history, people have hit upon technologies that multiply, indeed exponentiate the growth of knowledge,” said Stephen Pinker. They are inflection points in the history of our species.

Learning technology extends cognition

Where do the mind stop and the rest of the world begin? asked Chalmers and Clark in The Extended Mind. Their examples include the use of pens and computers to learn and do things.

Cognitive technologies, such as speech, writing, alphabets, mathematics, audio, video, and other media, allow our minds to directly create, express, manipulate, distribute and scale our thoughts. They also allow our minds to listen, read, understand, and learn. The pen, pencil and keyboard, and touchscreen can be seen as extensions of consciousness. As tools, they are almost part of one’s mind and body. Some philosophers have put forward this idea of extended consciousness or cognition as an alternative theory of consciousness. Once written, the object is then a piece of captured consciousness that can be read by others. It is this creation of another archived realm that allows us to escape the tyranny of time and place. The written word can be read at any time in any place. It becomes an object in itself, the creation separated from its creator. This is how learning escapes the tyranny of the human teacher.

Whether as a learner or teacher, learning technology extends the mind in several dimensions. First and foremost, it changes long-term memory, the fundamental aim of learning. With that change, we escape the tyranny of time by being able to think about the past as history and future possibilities. The faculty of imagination is fuelled by knowledge and skills. We can also escape the tyranny of location and learn about places beyond our place of residence, even the planet. It also provides the ability to learn to read and write, learn other languages, appreciate other cultures, learn mathematics, science, appreciate art. Some see learning technology as literally an extension of consciousness, the pen or smartphone as part of consciousness.

Many technologies were extensions of and protection of our bodies, clothes, homes, domestic appliances, tools, weapons, transport, glasses. Some, however, are extensions of the mind; pens, pencils, erasers, print, books, computers, smartphones, digital assistants, and so on. The language appeared millions of years ago, writing thousands, printing hundreds, broadcast media a hundred or so years, and in just decades we’ve seen computers, the internet, and AI appear. We are now seeing the exponential invention of ‘cognitive’ technologies.

Learning technology replaces teaching with learning

This is a matter of degree but there can be no doubt that writing, printing, broadcast media, computers, the internet, and now AI and data, have to different degrees replaced the need for a physical teacher. Most learning no longer takes place in the presence of a teacher but in other contexts made possible by learning technology. We read, write, watch videos, hear podcasts, access the vast knowledge base on the internet, and now use voice assistants, all in the absence of other humans. With each successive stage of learning technology, the act of learning has changed. The locus of learning has shifted away from the live, synchronous teacher to asynchronous learning. This is an uncomfortable truth for many but a truth nevertheless.

Learning technology scales

As learning technology evolved, its scalability increased. As soon as we invented writing, we freed learning from the tyranny of space and time. Knowledge could be captured, distributed, and be made available to many others in different places, at different times. This was accelerated with printing, as identical copies could be mass-produced and distribution could take place at national and international levels. This scaled-up even more with broadcast media, such as radio and TV. It went global with the internet. Another dimension of scalability is now possible with personalization, AI, and data-driven learning.

Learning technology is enabling new pedagogies

New pedagogic techniques are enabled by new learning technologies. The humble pencil and eraser allow one to fail, erase and retry, allowing one to fail as one learns. That is a good, simple example of how a piece of technology can enable a pedagogic advance. Cutting, pasting, spellcheck, grammar check, synonym-selection and editing tools all help one to write. Allowing learners to do cognitively effortful learning on a computer allows them to do things on their own free from doing it at a specific time and place. AI and data-driven systems allow pedagogic techniques like personalization, deliberate practice, spaced practice, and motivational pedagogies such as behavioral, nudge theory to be delivered. In general, there is a wider set of pedagogic options that can be blended into an optimal blend suited to specific target groups of learners, and types of learning.

Learning technologies draw from consumer technologies

Few technologies were ever designed specifically for learning. Early writing was almost exclusively used to ‘account’ for the products of agriculture and other goods, exchanges and treaties. Manuscripts were for religious consumption only. Printing was initially restricted to Biblical and other related texts. Broadcast media were mostly about news and entertainment. Computers were, and are still largely, the technologies of work and play. The internet became an agora for information, e-commerce and social media. AI and data are used for practical purposes in the mediation of interfaces such as Google, social media, Netflix, and Amazon. They have all been used for teaching and learning but this came later and on a smaller scale. But size is not everything. It is the fact that they could be used to learn that gave all of these technologies real potency. In every case, learning turned out to be the killer application.

Learning technology melds hardware and software

The tendency is to see technology in mechanical, material terms. We see this in the many books about technology, such as User's The History of Mechanical Invention. The word technology comes from the Green Techne (art, craft) and logia (writings). My definition of ‘learning technology’ includes both hardware and software, as almost every piece of modern hardware has, as an integral part of its function or output, something that can loosely be called software. The hardware of pens, pencils, erasers and paper have writing as their software. Printed books and papers have writing and images as their software. YouTube has video content as its software. The internet has every media type as its software, as well as code that controls the logic of delivery. In all of these cases, the hardware is inseparable from the software and must be seen as being entwined as technology. Of course, in most discussions of technology, there is a bias towards physical, tangible, mechanical devices, but as James Gleick has shown in his book ‘The Information’, many of our most important advances have been intangible technologies, such as writing, alphabet, audio, images, video, software, and social media.

Brian Arthur's The Nature of Technology sees combinations of technology as the deep driver of technological innovation. It is especially true of learning technology. The long history of learning technologies from the pigments, brushes, pots, and lamps used in cave drawings have always been combinations. Writing instruments, whether brushes, pens, or pencils, need pigments or inks to transfer writing to yet another form of technology, papyrus, parchments, or paper, which in turn allow is to replicate texts as software to be read. Combinations of different technologies - presses, metallic letters, ink, and paper - gave us printing, with copied texts being the readable software. The computer brought together different media; text, graphics, audio, video, animation, and now VR and AR in a combined, multimedia device as media of the mind. The internet brought these to a global audience for learning, with Wikipedia for text, YouTube for video, podcast services for audio, and many other free learning resources, such as MOOCs, Duolingo, Khan Academy. So learning technology has always been a combination of technologies, physical and psychological, right through to the modern smartphone, which is a learning device among other things.

Learning technology can be both good and bad

The pen may be mightier than the sword but most technology, also learning technology, still has ethical consequences. These ethical dimensions are fairly muted compared to weaponry, the carnage of driving cars, and fossil fuels in engines. We continue to drive cars in the face of the indisputable fact that nearly 1.5 million people die horrific deaths every year in car crashes. The figure for those maimed and injured is much higher. This is the casualty equivalent of an annual World War. Many other technologies have similar ethical dichotomies. Plastic syringes may save lives but plastic may be killing our oceans. Bitcoin and blockchain may be innovative technologies but as havens for money laundering and tax evasion, along with energy needs harmful to climate change, are a consequence. Learning technologies are nowhere near as lethal but few would deny that there are downsides. Paper production means felling trees and is one of the most polluting manufacturing processes we have ever invented. Billions of plastic bricks have been produced by a famous Danish toymaker. The internet and AI have given us ethical issues that are well known.

Even the humble LMS has had a genocidal side. Tom Watson, CEO of IBM flew to meet Hitler in 1939 and sold him a primitive, punch-card, Learning Management System, called the Hollerith system. As told in the excellent book IBM and the Holocaust by Edwin Black, it stored data on skills, race, and sexual orientation. Jews, Gypsies, the disabled and homosexuals, were identified and selected for slave labor and death trains to the concentration camps.

Although some present education is always intrinsic good, it is not without its ethical problems. At one level it can be argued that it sometimes increases social inequalities and uses time and money that would be better spent elsewhere. It has also been used for ideologies that have proved harmful, at the outer extremes of the political spectrum. The hideous genocides of Stalin, Mao Tse Tung and Pol Pot were the result of an educated class producing ideas that were used to literally eliminate entire strata of populations. The learning technology of propaganda has been used to educate and, even worse, ‘re-educate’ dissenters. It is clear, then, that learning technology is not always an intrinsic good. It can be a pedagogic trap, even destructive force.

Learning technology gets declassified

We also have to recognize that what we see as technology tends to fade and be declassified as technology. Anything invented before we were born is often seen as just being there and not categorized as technology. So writing, pens, pencils, erasers, and paper are de-technologized. Books, magazines, and printed materials are seen as worthy precursors to new technology, ignoring the fact that they were the technology of their age and seen with similar levels of suspicion and condemnation. Chalkboards are worthy, PowerPoint is unworthy. Broadcast media such as film, radio, and TV have now achieved that standing as a technology with a certain prestige, compared to new media. Even more traditional services on the internet are regarded with fondness when compared to the products of AI and data. Time is a great healer and the technology of the day now quickly becomes the technology of yesterday and not really technology at all.

In my next piece, I outline the history of learning technology. This will be followed by pieces on each stage.

Stage 1 Prehistory. Stage 2 Writing, Stage 3 Printing, Stage 4 Broadcast media, Stage 5 Computing, Stage 6 Internet, Stage 7 AI and data

March 14, 2021

Starlink changes everything-It may be the most important form of learning technology of the century

Starlink changes everything-It may be the most important form of learning technology of the century

I was out in my garden in May last year, watching a stream of satellites pass in a line overhead. It was beautiful. Starlink changes everything. Online learning, it absolutely changes everything. A global network of satellites delivering high-speed broadband means that anyone, anywhere in the world can get high-speed broadband.

How does it work?

There are over 1000 satellites up already. Target is 2027 for thousands of more satellites. Why so many? Well, each has a small cone of coverage but it cuts latency. Lasers between satellites travel at the speed of light. This is much faster than optical delivery through cable and allows global distribution with very low latency. Note that this will not wipe out urban networks but is great for rural and low-density markets. If you are worried about space debris, their satellites have propulsion, collision software and can be dropped to disintegrate when they come to the end of their life.

What does it cost?

Prices at the moment are £89 plus £439 for the dish and speeds at 50-150mbs. However, speeds will soon double and prices will fall. It has over 10,000 users in its US beta program and is also delivering services to users in the UK. You can sign up right now.

How did we get here?

It’s less than 30 years since Tim Berners-Lee invented the world-wide-web in 1991. There was no broadband 20 years ago from today. It started in the UK on 31 March 2000 and for years it was kilobits then just 2megabits by 2005. 50 megabits were introduced in 2008, 100 by 2010. This was an amazing achievement and has revolutionised play, work and learning.

1G networks were the first, 2G networks added data for things like SMS messages, 3G internet added even more and 4G, what we currently use, much faster internet access that has enabled social media and streaming. With every gear, change comes faster and more efficient delivery. 5G delivers much, much higher speed and bandwidth. 4G caps out at 100 megabits per second (Mbps), 5G caps out at 10 gigabits per second (Gbps). That means 5G is x100 faster than 4G technology, theoretically at least.

But what does this Starlink move mean?

To be honest, this is not really about 5G. Starlink is more important than 5G. It allows us to work and learn anywhere. It will allow people to move out of cities. High bandwidth, low latency, reliable internet will change how we work and learn. Its timing is perfect with respect to Covid. Now that we've been through the Great Pause and learnt to work and learn more at home, Starlink accelerates this process.

Rural business

First, It’s a great leveller. It delivers high-speed broadband to all rural areas, allowing work to migrate out of the cities, also boosting local businesses. Broadband will. No longer be an urban thing. This is in line with the political demands in countries where populations have voted for fewer globalisations and urbanisation, in favour of a more geographically, equal distribution of wealth. SpaceX had to reach certain delivery speeds in order to participate in the Federal Communication Commission's up to $16bn Rural Digital Opportunity Fund.

Developing world benefits

More than this, it allows broadband to be delivered to anywhere on the planet. This includes the whole of the developing world. This is mind-boggling and may free up the talent in those economies, bringing them into the fold. Anyone can produce anything and sell their talents online. The local becomes global.

Global online learning

Post-Covid, the world will undoubtedly have taken a shift towards online learning in schools, colleges, universities and the workplace. Forget the conspiracy theories, 5G wireless technology stands for ‘fifth generation’ cellular technology. Tie this up with Starlink, a low earth orbit network of satellites delivering blistering speeds to everywhere in the world and the engine that is AI, and we have a perfect storm that will transform global, online learning.

SpaceX's satellite internet system will offer still blazingly fast speeds of up to 1 gigabit per second. It will offer satellite internet to the entire planet, including remote locations where internet isn't currently available. Its satellites are low enough, and move (not geostationary), to deliver this with no blindspots. That’s an astounding leap. A couple of orders of magnitude better and global coverage. In terms of delivery and the user experience in online learning, this means a lot. In short, we can get online. learning anywhere.

Ultra-low latency

We spend a lot of time watching that little circle spinning on our screens. Technically it’s called latency, the time taken to find, identify and transfer data. 5G will make this all but disappear. This matters when you’re delivering complex online learning, whether it’s video, simulations, AI, VR or AR.

The Media Equation: How People Treat Computers, Television and New Media Like Real People and Places by Byron Reeves and Clifford Nass, two Stanford academics, is full of juicy research on media in learning. It provides a compelling case, backed up with empirical studies, to show that people confuse media with real life. This is actually a highly useful confusion: it is what makes movies, television, radio, the web and e-learning work. But their research also supports the case for 5G. 35 psychological studies into the human reaction to media all point towards the simple proposition that people react towards media socially even though, at a conscious level, they believe it is not reasonable to do so. They can't help it. In short, people think that computers are people, which makes online learning work.

Why is this relevant to 5G? Well in real life we live in real-time. We don’t encounter little spinning circles, except when waiting on a late train or in a queue, and who wants that? Hearteningly, it means that there is no reason why online learning experiences should be any less compelling - any less 'human' in feel - than what we experience in the real world and the classroom. As long as media technology is consistent with social and physical rules, we will accept it. Read that last part again, 'as long as a media technology is consistent with social and physical rules'. If media technology fails to conform to these human expectations - we will very much not accept it.

The spell is easily broken. Nass & Reeves showed that unnatural ‘pauses’ inhibit learning. If the media technology fails to conform to our human expectations - we will NOT accept it. This is a fascinating lesson for online learning. We must learn to design our courseware as if it were being delivered in real-time by real people in a realistic fashion. The effectiveness of the user experience on an emotional level will depend as much on these considerations as on the scriptwriting and graphic design. It all has to work seamlessly, or the illusion of humanity fails. This has huge implications in terms of the use of media and media mix.

A simple finding, that shows we have real-life expectations for media, is our dislike of unnatural timing. Slight pauses, waits and unexpected events cause a disturbance. Audio-video asynchrony, such as poor lip-synch or jerky low frame-rate video, will result in negative evaluations of the speaker. These problems are cognitively disturbing. The lower learning. All that disappears with 5G.

Flawless streaming

Streaming will become much easier and almost flawless, allowing online learning to deliver whatever media is necessary at whatever time is optimal for learning. Note that this does not open the floodgates for over-engineered multimedia in learning, Media-rich is not necessarily mind-rich. Many see video as the killer app for 5G. It is one but the video is rarely enough on its own in learning. It will certainly boost LXP, personalised delivery of any media type.

AI mediated learning

AI delivered learning will also be easier as realtime calls to cloud-based AI services opens up smart solutions in learning. This opens up a new world for adaptive learning, feedback, chatbots, automated notifications based on xAPI, learning in the workflow. Specifically, it allows access to services, such as OpenAI API to tap into AI on demand. This means smarter, faster and better online learning. We free ourselves from the current presentation of flat, linear experiences. The process and learning is not an event but a process that will be sensitive to each individual learner. Personalised learning becomes a reality. I mention Starlink in my book AI for Learning.

New user experiences

New user experiences and processes will be possible when we free ourselves from the tyranny of latency and slow speed internet. The promise of blended learning that can deliver great simulations, immersion and whatever one has delivered in the real world or classroom is now possible. New business models will emerge. New forms of learning with full immersion, AI, personalisation will emerge.

New devices

Rumours have it that Apple will be offering a ‘glasses’ or AR device. In any case, wearables, watches and small devices are now everywhere. 5G allows high-speed access to and from these devices. This is not just about smartphones, it frees up fast internet speeds to all devices. We can link learning to devices that provide the context for learning. Where are you, what are you doing, then this is what we can do to help? This all becomes possible wherever you are indoors or outdoors, anywhere on the planet.

Conclusion

The great leaps in learning technologies were writing, the alphabet, printing, broadcast media, computers, the internet and now AI and data. But this is the Internet with a difference. It's universal and global. Higher performance and improved efficiency empower new user experiences and connect new industries. This is not about boosting learning. It is about changing the very nature of education and learning. The implications for the poorer regions of the world are obvious, as it could be a great leveller. The tides rise with the gravitational pull of the moon, this is a rising tide that also comes from space, for everyone, one that doesn’t ebb.

March 13, 2021

functional groups and- upper limb function in patients after pediatric arterial

Participants were recruited from 2014(functional groups) through 2017 as part of the Hemispheric Reorganisation (HERO) study48, approved by the Research Ethics Committee of Bern, Switzerland. Patients were identified by the Swiss Neuropaediatric Stroke Registry (SNPSR)—a multicenter, prospective, and population-based registry that includes children diagnosed with AIS under the age of 16 years1. Patients met the following inclusion criteria: Diagnosis of AIS (confirmed by MRI or computed tomography) before the age of 16 years and at least two years prior to recruitment in the chronic phase, and older than five years of age to enable adequate compliance, and contralateral corticospinal tract wiring. For more detailed information see the previously published study protocol48. The control group, a sample of typically developing peers comparable in age and sex to the patients’ groups, was recruited through advertisements on the hospital intranet and flyers. Participants were excluded if they had neurological disorders unrelated to AIS, ferrous implants, active epilepsy, claustrophobia, developmental delay, or behavioral problems that could affect their ability to comply with study requirements (see Supplementary Fig. S1).

Of the 379 patients identified from the SNPSR who met the inclusion criteria, 96 were not contacted: 20 had died, 7 had either trisomy 21, epilepsy, other severe handicaps, or severe behavioral problems, 12 were living abroad, and for 57 consent for SNPSR or follow-up studies was lacking. Of the remaining 283 patients who were contacted personally by mail and subsequently by phone, 120 did not respond and 135 reported a lack of motivation or felt that the duration and type of assessment (MRI, TMS) would be inconvenient. Of the remaining 28 patients, 2 had to be excluded because of developmental delay or behavioral problems interfering with compliance with the test conditions, 2 had an erroneous fMRI sequence, and 2 retainer artifacts. Another 4 were excluded because of the lesion being bilateral. Thus, the final sample consisted of 18 patients diagnosed with chronic AIS (see Supplementary Fig. 1). We matched the control group of typically developing peers using a 1:1 ratio by age and gender (n = 18).

All participants, or their parent or guardian if they were younger than 18 years, gave written informed consent, according to the Code of Ethics of the World Medical Association (Declaration of Helsinki).

Clinical outcome assessment

All participants underwent a standardized neurological examination performed by a research physician (J.D and S.G) at the Children’s University Hospital, Inselspital, Bern, Switzerland. Details of data collection and study design have been previously reported48. To study motor outcome, an extended and standardized test battery was adopted to investigate several domains as proposed by the International Classification of Functioning, Disability and Health framework45, including body structure (anatomical structure of the body), and body function (a physiological function of the body), activity (execution of a task or action), and participation (involvement in everyday life situations).

Disease-specific outcome

The Pediatric Stroke Outcome Measure (PSOM)49 was used to measure disease-specific outcomes at the time of MRI scanning. The PSOM assesses neurological deficits and consists of five subscales for right and left sensorimotor functioning, language production, language comprehension, and cognition/behavior. We used the sensorimotor subscale to classify the presence of hemiparesis (0 = no sensorimotor deficit; 0.5 = mild deficit, with normal function; 1 = moderate deficit, with decreased function; 2 = severe deficit with no function). Patients with scores greater than 0.5 on the sensorimotor subscale were classified as having hemiparesis50,51. Patients with a score of zero on all subscales were classified as having a good clinical outcome. Detailed information on each participant is provided in Supplementary Table S2.

Upper limb function

Hand strength. Palmar grasp strength and thumb–forefinger pinch strength was measured with a dynamometer (30 Psi pneumatic dynamometer Baseline, USA and a 30 lb mechanical pinch gauge, Baseline, USA). Participants were instructed to perform each task with maximal effort (repeating it three times with a 30-s break between attempts). After performing the task with each hand separately, the maximum values for both hands were averaged to yield a total hand strength score (HSS).

Quality of upper limb function. Quality of upper limb function was assessed using the Melbourne Assessment of Unilateral Upper Limb Function, version 252,53. This test contains 14 tasks (e.g. grasping and releasing, manipulating, reaching) that cover four basic upper limb functions, namely, range of movement, the accuracy of reaching and pointing, dexterity of reaching and manipulating, and fluency of movement. The assessment was performed for both the left and the right sides. The values of all items were summarized to yield a total Upper Limb Movement Quality Score (ULMQS) for each side.

Asymmetry of upper limb function. Using the assessment of HSS and ULMQS for both upper limbs allowed us to calculate the asymmetry between the dominant and non-dominant hand51 with the following Eq. (1):

$$Asymmetry index=\frac{scor{e}_{dominant side}-scor{e}_{nondominant side }}{scor{e}_{dominant side}+scor{e}_{nondominant side}}100$$(1)

An asymmetry index of zero represents perfect symmetry between the dominant and non-dominant side, whereas an index larger than zero represents asymmetry towards the dominant side.

Manual ability in everyday life

Manual ability in everyday life situations was assessed using the ABILHAND-Kids questionnaire54. This is a parent-reported outcome measure assessing the use of the upper limbs in everyday situations (0 = impossible, 1 = difficult, or 2 = easy to perform). The total score ranges from + 6.68 (all items easy to perform) to − 6.75 (all items impossible to perform).

Cortical Reorganization

Transcranial magnetic stimulation (TMS) was performed to determine the type of cortical reorganization after AIS11. For this purpose, silver-silver chloride surface electrodes (ALPINE, Biomed) were mounted in a tendon-belly arrangement over the Abductor Pollicis muscle on both hands55. A Neurodata amplifier system connected to an IPS230 Isolated Power System (Grass-Telefactor, Braintree, MA, USA) was used for pre-amplification (1000x) and as a bandpass filter (10–1000 Hz) of the EMG signals. The inputs were entered into a computer-assisted data acquisition system (sampling rate 5 kHz)56. The EMG signal peak-to-peak amplitudes were calculated for all derived muscles in a 65 ms time window. Single-pulse monophasic TMS pulses were delivered over both hemispheres. Both hemispheres were examined according to the stimulation response in the contralateral and/or ipsilateral upper extremity.

The cortical reorganization was defined according to the stimulation response in the ipsilateral or contralateral Abductor Pollicis Brevis muscle11: a stimulation response only in the contralateral Abductor Pollicis Brevis muscle were defined as contralateral reorganized, stimulation response only in the ipsilateral Abductor Pollicis Brevis muscle was defined as ipsilateral reorganized, and stimulation response in both the ipsilateral and contralateral Pollicis Brevis muscle was defined as mixed. The methodology was carried out in accordance with the safety regulations and guidelines57 and is described in detail in the Supplementary Material.

Neuroimaging

The MRI protocol was carried out in accordance with the safety regulations and guidelines48,58. All MRI recordings were performed on a 3 T scanner (Magnetom Verio, Siemens, Erlangen, Germany) equipped with a 32-channel phased-array head coil at the Inselspital, Bern University Hospital, Switzerland.

Structural MRI data

High-resolution anatomical T1-weighted images were acquired with a magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence with the following parameters: repetition time (TR) = 2530 ms; echo time (TE) = 2.92 ms; inversion time (Ti) = 1100 ms; flip angle (FA) = 9°; field-of-view (FOV) = 256 mm × 256 mm; matrix dimension = 256 × 256; isotropic voxel resolution = 1 mm3; with a total of 160 sagittal slices.

Lesion characteristics such as location, size, and side affected were obtained from anatomical images (T1) by a board-certified neuroradiologist (N.S.). Lesions were classified according to the hemisphere affected (left, right, or bilateral) and anatomical location (cortical, subcortical, or both cortical and subcortical). To calculate the volume of affected brain tissue in the chronic stage, ischemic lesions were manually traced on T1 weighted images acquired the same day as functional imaging. Lesion size was defined as the affected brain tissue in relation to the total brain volume (lesion volume [cm3]/total brain volume [cm3]). Total brain volume was calculated using the statistical parametric mapping toolbox (SPM12, Wellcome Department of Imaging Neuroscience, London, England).

Functional MRI data

The BOLD rs-fMRI images were recorded with a multiband echo planar imaging T2*-weighted sequence “MB-EPI” (Feinberg et al., 2013) and had the following parameters: TR = 300 ms; TE = 30 ms; FA = 90°; FOV = 230 × 230 mm; pixel size = 3.6 × 3.6 mm; matrix dimension = 64 × 64; 32 slices positioned in the line between the anterior and posterior commissure (interleaved ascending acquisition order); slice thickness = 3.6 mm; isotropic voxel resolution of 3.6 mm3; and a total of 1000 images were recorded. The fMRI time-series were acquired with a 2 GRAPPA acceleration factor and a 3D prospective acquisition correction mode.

Data were pre-processed using the functional connectivity toolbox (CONN, version 17)59 as implemented on the platform MATLAB (R2017; MathWorks, Natick, MA, USA). We used the standard preprocessing pipeline59. First, functional images from patients with lesions in the right hemisphere (n = 3) were flipped along the midsagittal plane, so that the affected hemisphere corresponded to the left hemisphere in the whole patient sample. Second, by visual inspection, we verified that none of the patients with lesions involving the cortex (n = 3) had overlapped with our predefined ROIs in the motor network. There were no cortical lesions overlapping with regions included in the analysis. Third, the structural images were segmented to allow the creation of white matter and cerebrospinal fluid (CSF) masks. The spatial preprocessing of the functional images included the correction of slice time, realignment, normalization, and smoothing (applying the Gaussian filter kernel, FWH = 8 mm). The quality of registration and parcellation was assessed by visual inspection of each subjects’ data. Fourth, the temporal processing of the functional images took into account potential confounding factors, such as movement parameters and artifacts. BOLD signals obtained from white matter and CSF masks were also included. All these temporal confounding factors were regressed out from the functional images using a generalized linear model framework. Finally, the functional images were filtered using a “band-pass filter” (0.01–0.1 Hz).

Functional connectivity was assessed in predefined ROIs of an extended model of the motor network (Fig. 4). This extended model was based on the previous literature on stroke recovery in humans and animals32 and included the bilateral areas of M1, prefrontal cortex (PFC), dorsal premotor cortex (PMC), supplementary motor area (SMA), and superior parietal lobe (SPL). Together, these five ROIs represent a motor network with 14 intrahemispheric and two interhemispheric connections (Fig. 4). For data extraction from those ROIs, we used the brain parcellation atlas from the CONN toolbox59. For each participant, we extracted the mean time-series by averaging across all voxels in each ROI and computed bivariate correlation coefficients for each pair of ROIs. For further analyses, we Fisher z-transformed the correlation coefficients.

Figure 4

The regions of interest (ROI) of the motor network. The functional connectivity analyses included the following ROIs: the prefrontal cortex (PFC), dorsal premotor cortex (PMC), the primary motor cortex (M1), supplementary motor area (SMA), superior parietal lobe (SPL) (adapted from Sharma et al.32). Altogether, this motor network consists of 14 intrahemispheric and 2 interhemispheric connections.

Related to our hypotheses on alterations in intra- and interhemispheric connectivity, we calculated the indices of average network connectivity: (1) ROI-to-ROI correlation coefficients of all connections in the ipsilesional (7 connections) and contralesional hemisphere (7 connections) were averaged for each participant to obtain average intrahemispheric network connectivity of the ipsilesional and contralesional hemisphere, (2) ROI-to-ROI correlation coefficients between homologous regions (e.g. M1–M1 and PMC–PMC; two connections) were averaged for each participant to obtain average interhemispheric network connectivity.

Statistical analyses

To test our primary hypothesis that patients after AIS with hemiparesis have lower inter-and intrahemispheric functional connectivity compared with patients with good motor outcomes and typically developing peers, we used the non-parametric Kruskal–Wallis test, and the Mann–Whitney U-test for post hoc pairwise comparison.

To test our second hypothesis that asymmetry of upper limb function and manual ability (assessed by HSS, ULMQS, and ABILHAND-Kids) is related to average inter-and intrahemispheric network connectivity, we used partial Spearman correlation analyses with age at assessment, age at a stroke, and lesion size as covariates. For visualization of the results, we extracted the partial correlations’ residuals.

All analyses were performed using the statistical software package R 3.6.060. To account for the effects of multiple hypothesis testing (type I error), false discovery rate (FDR) correction was employed. Results of P < 0.05 FDR-corrected were considered significant.