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Sunday, July 15, 2018

Can a Computer Diagnose Autism?

From Spectrum News

By Jeremy Hsu
July 1, 2018

Machine-learning holds the potential to help clinicians spot autism sooner, but technical and ethical obstacles remain.


Martin Styner’s son Max was 6 by the time clinicians diagnosed him with autism. The previous year, Max’s kindergarten teacher had noticed some behavioral signs. For example, the little boy would immerse himself in books so completely that he shut out what was going on around him. But it wasn’t until Max started to ignore his teacher the following year that his parents enlisted the help of a child psychologist to evaluate him.

Max is at the mild end of the spectrum. Even so, Styner, associate professor of psychiatry and computer science at the University of North Carolina at Chapel Hill, wondered if he had been fooling himself by not seeing the signs earlier. After all, Styner has studied autism for much of his career.

Given how complex and varied autism is, it’s not surprising that even experts like Styner don’t always recognize it right away. And even when they do spot the signs, getting an autism diagnosis takes time: Families must sometimes visit the nearest autism clinic for several face-to-face appointments. Not everyone has easy access to these clinics, and people may wait months for an appointment.

That reality has led to a detection gap: Although an accurate diagnosis can be made as early as 2 years of age, the average age of diagnosis in the United States is 4. And yet the earlier the diagnosis, the better the outcome.

Some researchers say delays in autism diagnosis could shrink with the rise of machine learning — a technology developed as part of artificial-intelligence research. In particular, they are pinning their hopes on the latest version of machine learning, known as deep learning.


“Machine learning was always a part of the field,” Styner says, “but the methods and applications were never strong enough to actually have clinical impact; that changed with the onset of deep learning.”

Deep learning’s power comes from finding subtle patterns, among combinations of features, that might not seem relevant or obvious to the human eye. That means it’s well suited to making sense of autism’s heterogeneous nature, Styner says. Where human intuition and statistical analyses might search for a single, possibly nonexistent trait that consistently differentiates all children with autism from those without, deep-learning algorithms look instead for clusters of differences.

Still, these algorithms depend heavily on human input. To learn new tasks, they ‘train’ on datasets that typically include hundreds or thousands of ‘right’ and ‘wrong’ examples — say, a child smiling or not smiling — manually labeled by people. Through intensive training, though, deep-learning applications in other fields have eventually matched the accuracy of human experts. In some cases, they have ultimately done better.

“I think these approaches are going to be reliable, quantitative, scalable — and they’re just going to reveal new patterns and information about autism that I think we were just unaware of before,” says Geraldine Dawson, professor of psychiatry and behavioral sciences at Duke University in Durham, North Carolina.


Not only will machine learning help clinicians screen children earlier, she says, but the algorithms might also offer clues about treatments.

Not everyone is bullish on the approach’s promise, however. Many experts note that there are technical and ethical obstacles these tools are unlikely to surmount any time soon. Deep learning — and machine learning, more broadly — is not a “magic wand,” says Shrikanth Narayanan, professor of electrical engineering and computer science at the University of Southern California in Los Angeles.


When it comes to making a diagnosis and the chance that a computer might err, there are “profound implications,” he says, for children with autism and their families. But he shares the optimism many in the field express that the technique could pull together autism research on genetics, brain imaging and clinical observations. “Across the spectrum,” he says, “the potential is enormous.”

Bigger is Better

To make accurate predictions, machine-learning algorithms need vast amounts of training data. That requirement presents a serious challenge in autism research because most data relevant to diagnoses comes from painstaking — and therefore limited — clinical observations.


Some researchers are starting to build larger datasets using mobile devices with cameras or wearable sensors to track behaviors and physiological signals, such as limb movements and gaze.

In 2016, a European effort called the DE-ENIGMA project started building the first freely available, large-scale database based on the behaviors of 62 British and 66 Serbian children with autism. So far, this dataset includes 152 hours of video interactions between the children and either adults or robots.


“One of the primary goals of the project is to create a database where you can train machine learning to recognize emotion and expression,” says Jie Shen, a computer scientist at Imperial College London and DE-ENIGMA’s machine-learning expert.

Dawson’s team at Duke is also collecting videos of children with autism via a mobile app developed for a project called Autism & Beyond. During the project’s initial year-long run in 2017, more than 1,700 families took part, uploading about 6,000 videos of their children’s behaviors and answering questionnaires.


“We were getting in a year the amount of data that experts might get in a lifetime,” says Guillermo Sapiro, professor of electrical and computer engineering at Duke, who is working on the app’s next iteration.


The group is also training a deep-learning algorithm to interpret the actions in the video clips and detect specific behaviors — something Dawson describes as ‘digital phenotyping.’ At this year’s meeting of the International Society for Autism Research, Dawson presented results from a study of 104 toddlers, including 22 with autism, who watched a series of videos on a tablet.

The tablet’s camera recorded the child’s facial expressions and head movements. The algorithm picked up on a two-second delay in the autistic children’s response to someone calling their name. Clinicians could easily miss this slight lag, an important red flag for autism, Dawson says.

One caveat to this kind of an approach is that collecting data outside the structured confines of a lab or doctor’s office can be messy. Sapiro says he was puzzled by the algorithm’s assessment of one participant in the Autism & Beyond project, who showed a mix of developmentally typical and atypical behaviors.

When Sapiro watched the videos of that little girl, though, he quickly realized what was going on: Her behavior was typical during the day but atypical at night, when she was tired.

Researchers might be able to interpret these videos more readily by combining them with information from sensors capturing a child’s behavior. A group of scientists at the Georgia Institute of Technology in Atlanta is exploring this approach, which they describe as ‘behavioral imaging.’ One of the scientists, Gregory Abowd, has two sons on the spectrum.

“My oldest is a non-speaking individual, and my younger one speaks but has difficulty with communicating effectively,” Abowd says. In 2002, three years after his oldest son was diagnosed with autism at age 2, he says, “I started to get interested in what I could do as a computer scientist to address any of the challenges related to autism.”

The Georgia Tech scientists are investigating sensors to track a range of physiological and behavioral data. In one project, they are using wearable accelerometers to monitor physical movements that could signify problem behaviors, such as self-injury. Another initiative involves glasses fitted with a camera located on the bridge of the nose to make it easier to follow a child’s gaze during play sessions.

The dream, says computer scientist James Rehg, is to train machine-learning algorithms to use these signals to automatically generate a snapshot of a child’s resulting social-communication skills. “I think it’s a really exciting time and an exciting area precisely because of the wealth of signals and different kinds of information that people are exploring,” says Rehg.

Comprehensive behavioral data could also yield clues about conditions that often co-occur with autism, says Helen Egger, chair of the child and adolescent psychiatry at NYU Langone Health in New York City. Egger says larger datasets may help make sense of the overlap in behavioral traits between autism and conditions such as anxiety, attention deficit hyperactivity disorder, obsessive-compulsive disorder and depression.

“We have to be able to use these tools with the full spectrum of children to differentiate children with autism from those without autism,” she says.

"Across the spectrum, the potential is enormous."
-- Shrikanth Narayanan

The Earliest Signals

Some research teams hope to train machine-learning models to detect signs of autism even before behavioral symptoms emerge.

Styner and his colleagues in the Infant Brain Imaging Study (IBIS), a research network across four sites in the U.S., are using deep learning to analyze the brain scans of more than 300 baby siblings of children with autism. Because these ‘baby sibs’ are known to be at an increased risk of autism, it might be easier to spot signs of the condition in this group.


In 2017, IBIS published two studies in which machine-learning algorithms picked up on certain patterns in brain growth and wiring and correctly predicted an autism diagnosis more than 80 percent of the time.

“One key difference between our studies and many machine-learning studies is that ours have been predicting later diagnostic outcome from a pre-symptomatic period,” says Joseph Piven, professor of psychiatry and director of the Carolina Institute for Developmental Disabilities at the University of North Carolina at Chapel Hill and an IBIS investigator. “That will clearly be useful clinically, if replicated.”

Machine learning trained on brain imaging might also provide more than a binary ‘yes’ or ‘no’ prediction about a diagnosis, Styner says. It could also forecast where that child falls along on the autism spectrum, from mild to severe. “That’s what we’re heading for, and I see in our research and in other people’s research that that’s definitely possible,” he says.

One factor limits the volume of brain-imaging data that can be collected: Participants have to find magnetic resonance imaging machines, which are bulky, expensive and tricky to use with children. A more flexible option for detecting early signs of autism may be electroencephalography (EEG), which monitors electrical activity in the brain via portable caps studded with sensors.


“It was and still is the only brain measurement tech that can be used widely in clinical care practice,” says William Bosl, associate professor of health informatics, data science and clinical psychology at the University of San Francisco.

Machine-learning algorithms represent just the first part of the equation in working with EEG. The second involves what Bosl describes as the “secret sauce” — additional computer methods that remove noise from these signals and make it easier to detect patterns in the data. In a 2018 study, Bosl and his colleagues used this algorithmic mix to monitor the EEGs of 99 baby sibs and 89 low-risk infants for almost three years.


Using EEG data from babies as young as 3 months, the method was able to predict severity scores on the gold-standard diagnostic test, the Autism Diagnostic Observation Schedule (ADOS).

Even when they are promising, algorithms reveal nothing about the biological significance of their predictive findings, researchers caution. “We don’t know what the computer is picking up in the EEG signal per se,” says Charles Nelson, director of research at Boston Children’s Hospital’s Developmental Medicine Center, who co-led the EEG work.


“Maybe it is a good predictive biomarker, and as a result we can make a later prediction about a later outcome, but it doesn’t tell us why children develop autism.”

Like researchers working with brain imaging or behavioral data, those focused on EEG are also relying on relatively small datasets, which comes with complications. For example, sometimes an algorithm learns the patterns of a particular dataset so well that it cannot generalize what it has learned to larger, more complex datasets, Bosl says. This problem, called ‘overfitting,’ makes it especially important for other studies — ideally by independent teams — to validate results.

Another common pitfall arises when researchers use training datasets that contain an equal number of children with and without autism, Styner says. Autism is not present in half of all children; it’s closer to 1 in 60 children in the U.S.


So when the al,gorithm moves from training data to the real world, its ‘needle-in-a-haystack’ problem — identifying children with autism — becomes far more challenging: Instead of finding 100 needles mixed in with 100 strands of hay, it must find 100 needles mixed in with 6,000 strands of hay.

Computer Assist

Given these challenges, many autism researchers remain hesitant about rushing to commercialize applications based on machine learning. But a few have more willingly engaged with startups — or launched their own — with the goal of bypassing the autism-screening bottleneck.

Abowd has served as chief research officer for a Boise, Idaho-based company called Behavior Imaging since 2005, when Ron and Sharon Oberleitner founded the company, almost a decade after their son was diagnosed with autism at age 3. The company offers telemedicine solutions, such as the Naturalistic Observation Diagnostic Assessment app, which allows clinicians to make remote autism diagnoses based on uploaded home videos.

Behavior Imaging is partway through a study that aims to train machine-learning algorithms to characterize behaviors in videos of children. Once they identify the behaviors, they could draw clinicians’ attention to those key timestamps in the video and spare them from having to watch the video from start to finish. In turn, clinicians could improve the algorithm by either confirming or correcting its assessments of those moments.


“This is going to be a clinical-decision-support tool that is going to constantly build up the industry expertise of what atypical behaviors of autism actually are,” Ron Oberleitner says.

A more ambitious vision for computer-assisted autism screening comes from Cognoa, a startup based in Palo Alto, California. The company offers a mobile app that provides risk assessments to parents based on roughly 25 multiple-choice questions and videos of their child’s activities. Ultimately, Cognoa’s leaders want U.S. Food and Drug Administration approval for an application that they say will empower pediatricians to diagnose autism and refer children directly for treatment.



Dennis Wall, now a researcher at Stanford University, founded Cognoa in 2013. After two studies published in 2012, he says, he became convinced that his machine-learning algorithms could be trained to make autism diagnoses more accurately and faster than two screening tools, the ADOS and the Autism Diagnostic Interview-Revised (ADI-R). “It was a solid step forward and provided a sound launch pad for future work,” Wall says.

But Wall’s 2012 papers didn’t convince everyone. Several critics, including Narayanan, pointed out in a 2015 analysis that the studies used small datasets and only considered children with severe autism, excluding the most complicated and difficult-to-diagnose forms of the condition. In the real world, they argued, his algorithms would miss many diagnoses a clinician would catch.


Wall published a 2014 validation study that he says expanded the test set to include data from children in the middle of the spectrum. He acknowledges that the 2012 studies used smaller datasets, but says the accuracy of his algorithms holds up in larger datasets used in later studies.

In 2016, Narayanan and some of his 2015 co-investigators described their own efforts to use machine learning to streamline autism screening and diagnosis. In their conclusion, they sounded a note of caution, saying that their algorithms, trained on responses from parents seeking a diagnosis for their child, also performed well but needed more testing in larger and more diverse populations.


“I feel that there is a clear potential to fine-tune associated clinical-instrument algorithms with machine learning,” says co-investigator Daniel Bone, senior scientist at Yomdle, Inc., a technology startup based in Los Angeles and Washington, D.C.

“However, I’ve not seen clear evidence yet — my own work included — that this approach is a monumental step beyond the traditional statistical methods that have been employed by researchers for decades.”

Merely amassing data to train machine-learning algorithms won’t necessarily help, says Bone’s collaborator, Catherine Lord, director of the Center for Autism and the Developing Brain in White Plains, New York, who developed the ADOS. Sometimes there are obvious but unacknowledged explanations for an algorithm’s apparent success, Lord adds.


For example, boys are diagnosed with autism about four times more often than girls. A machine-learning study that appears to succeed in predicting the difference between people with and without autism may in fact be noticing nothing more than gender differences. Likewise, it might be picking up on differences in intelligence.

“It isn’t the machine learning’s fault,” Lord says. “It’s the human reviewers and the general idea that if you have enough study subjects you can do anything.”

“Just because it's couched in mathematics doesn’t mean it's more real.”
-- Fred Shic

Are we there yet?

Some teams claim that machine learning can predict autism with accuracies well beyond 95 percent, but those rates are unlikely to hold up under more rigorous test conditions, researchers say. Until the algorithms are that good, they are nowhere near ready for clinical use — and they won’t get that good without more experienced diagnosticians helping to guide their development: It takes clinical expertise to recognize and avoid the more obvious pitfalls in interpreting the available data.

“By and large, I think the biggest problem we have is people with data-mining expertise going to datasets they don’t comprehend, because they’re not being guided by a clinical perspective,” says Fred Shic, associate professor of pediatrics at the University of Washington in Seattle.

“I think it’s really important to work together when extracting deep truths; we need people with understanding of all sides to sit together and work on it.” Journal editors, too, should find reviewers with machine-learning expertise to look over related autism studies, he says.

Shic is co-investigator on a project that has developed a tablet-based app called the Yale Adaptive Multimedia Screener, which uses video narration to walk parents through questions about their child’s behavior. “I think it has a lot of advantages,” he says, but adds, “I don’t want to oversell it because honestly there are so many ways you can go wrong with these things.” To learn more, he said, researchers need larger studies with long-term follow up.

Shic says he makes a habit of scrutinizing the methods other researchers use and also checks to see if they replicate their algorithm’s accuracy using an independent dataset. “Of course, we will see a lot of advances. We will also see a lot of snake oil,” he says. “So we’ve got to be vigilant and suspicious and critical like we’ve been about everything that comes up; just because it’s couched in mathematics doesn’t mean it’s more real.”

Mathematics will never solve the ethical problems that may come with using machine learning for autism diagnosis, others note. “I really don’t think we should put the power of diagnosis, even early diagnosis, in the hands of machines that would then relay the results from the machine to the family,” says Helen Tager-Flusberg, director of the Center for Autism Research Excellence at Boston University.

“This is very profound moment in the life of a family when they are told that their child has the potential to have a potentially devastating neurodevelopmental disorder.”

Styner points to the chance of false positives, or instances when a computer might wrongly identify a child as having autism, as a reason to move slowly. “I actually think something like Cognoa would be doing harm — significant harm — if it incorrectly predicts that the kid would have autism when [he or she] does not,” he says.

“Unless you have a rock-solid prediction, I can’t see how this is not something unethical to a certain degree because of those false positives.”

In Styner’s own family, things turned out better than he might have predicted. His son Max, now 11, is academically gifted and benefits from a social-skills class and weekly play group. He is doing so well, in fact, that he may no longer meet the threshold for an autism diagnosis, Styner says.

Given his experience as a parent, though, he understands why families are so eager for earlier screening and diagnosis — and it still motivates him in his efforts to hone the potential of machine learning. “I can really empathize with families and their interest in knowing not just the diagnosis, but also what to expect with respect to severity of symptoms,” he says. “I certainly would have wanted to know.”

Parents Who Had Severe Trauma, Stresses in Childhood More Likely to Have Kids with Behavioral Health Problems

From UCLA Health Sciences
via ScienceDaily

July 9, 2018

A new study finds that severe childhood trauma and stresses early in parents' lives are linked to higher rates of behavioral health problems in their own children.


The types of childhood hardships included divorce or separation of parents, death of or estrangement from a parent, emotional, physical or sexual abuse, witnessing violence in the home, exposure to substance abuse in the household or parental mental illness.

"Previous research has looked at childhood trauma as a risk factor for later physical and mental health problems in adulthood, but this is the first research to show that the long-term behavioral health harms of childhood adversity extend across generations from parent to child," said the study's lead author, Dr. Adam Schickedanz.

He is a pediatrician and health services researcher and assistant professor in the department of pediatrics at the David Geffen School of Medicine at UCLA.

The study showed that the children of parents who themselves had four or more adverse childhood experiences were at double the risk of having attention deficit hyperactivity disorder and were four time more likely to have mental health problems.

A mother's childhood experiences had a stronger adverse effect on a child's behavioral health than the father's experiences, the study found.

Parents who lived through adverse childhood experiences were more likely to report higher levels of aggravation as parents and to experience mental health problems, the researchers found. However, these mental health and attitude factors only explained about a quarter of the association to their child's elevated behavioral health risks.

The remainder of how the parent's adverse childhood experiences are transmitted to their child's behavior deserves further study.

The findings add to the evidence supporting standardized assessment of parents for adverse childhood experiences during their child's pediatric health visits.

"If we can identify these children who are at a higher risk, we can connect them to services that might reduce their risk or prevent behavioral health problems," Schickedanz said.

The researchers used information from a national survey containing information from four generations of American families, including information from parents about whether they were abused, neglected or exposed to other family stressors or maltreatment while growing up, and information on their children's behavior problems and medical diagnoses of attention deficit disorder.

With this data, they were able to find strong associations between the parents' adversity histories and their children's behavioral health problems, while controlling for factors such as family poverty and education level.

The next step for researchers is to look at how resilience factors, such as the support of mentors or teachers, could offset the harms of childhood traumas, Schickedanz said.

The study was published in the journal Pediatrics.

The research study was funded by the UCLA National Research Service Award Primary Care and Health Services Fellowship. The national survey data was collected by the Institute for Social Research at the University of Michigan with funding from grants from the National Science Foundation and the National Institutes of Health.

The study co-authors are Dr. Neal Halfon and Dr. Paul Chung from UCLA; and Dr. Narayan Sastry from the University of Michigan.

Journal Reference
  • Adam Schickedanz, Neal Halfon, Narayan Sastry, Paul J. Chung. Parents’ Adverse Childhood Experiences and Their Children’s Behavioral Health Problems. Pediatrics, 2018; e20180023 DOI: 10.1542/peds.2018-0023

Saturday, July 14, 2018

Protecting Your Kids from Failure Isn’t Helpful. Here’s How to Build Their Resilience

From The Conversation

By Mandie Shean
July 10, 2018

In recent years, there has been a concerted effort to protect children from failure in order to safeguard their fragile self-esteem. This seems logical – failure is unpleasant. It tends to make you look bad, you have negative feelings of disappointment and frustration, and you often have to start again.

While this is logical, it actually has the opposite effect. Children and adolescents in Australia appear less able to cope than ever before.

The problem is, in our efforts to protect children, we take valuable opportunities for learning away from them. Failure provides benefits that cannot be gained any other way. Failure is a gift disguised as a bad experience. Failure is not the absence of success, but the experience of failure on the way to success.

Related

The Gift of Coping

When we fail, we experience negative emotions such as disappointment or frustration. When children are protected from these feelings they can believe they are powerless and have no control over mastery.

The answer is not to avoid failure, but to learn how to cope with small failures. These low-level challenges have been called “steeling events”. Protecting children from these events is more likely to increase their vulnerability than promote resilience.

When adults remove failure so children do not have to experience it, they become more vulnerable to future experiences of failure.

Small failures can help your child become more resilient, if handled properly.

The Gift of Understanding Natural Consequences

One of the greatest gifts failure brings is we learn natural consequences to our decisions. It’s a very simple concept developed by early behaviourists: “when I do X, Y happens”. If I don’t study, I will fail; if I don’t practice, I may lose my spot on the team.

Allowing children to experience these outcomes teaches them the power of their decisions.

When parents and teachers derail this process by protecting children from failure, they also stand in the way of natural consequences. Studies show children who are protected from failure are more depressed and less satisfied with life in adulthood.

The Gift of Learning

Mistakes are the essence of learning. As we have new experiences and develop competence, it’s inevitable we make mistakes. If failure is held as a sign of incompetence and something that should be avoided (rather than a normal thing), children will start to avoid the challenges necessary for learning.

Failure is only a gift if students see it as an opportunity rather than a threat. This depends on their mindset.


Children with a growth mindset believe intelligence is malleable and can be changed with effort. Those with a fixed mindset believe they were born with a certain level of intelligence. So, failure is a signal for growth mindset children to try harder or differently, but a sign they aren’t smart enough for children with a fixed mindset.

Praise Should Be Focused on Effort

Praise can be used to compensate and help children feel valuable in the face of failure. We see this when children get a participation ribbon in a running race for coming in last.

But research indicates, paradoxically, this inflated praise has the opposite effect. In the study, when parents gave inflated praise (“incredibly” good work) and person-focused praise (such as “you’re beautiful”, “you’re smart” or “you’re special”), children’s self-esteem decreased.

Related

Praise that is person-focused results in children avoiding failure and challenging tasks to maintain acceptance and self-worth. This is because praise is conditional on “who they are” rather than their efforts.

Praise for effort sounds like “you worked really hard”. This is better because children can control how hard they work, but they can’t control how smart or special they are. Children need to be free to learn without there being a risk to their sense of worth.

Tips for Parents

So how do we do this well? Here are some tips to help parents support their children:


Protecting your child from failure isn’t actually helpful. Allow them to feel and live it, and let them have the gifts failure brings. Experiencing failure will make them more resilient and more likely to succeed in the future.

Why Males are More At Risk than Females for Neurodevelopmental Disorders

From the University of Maryland School of Medicine
via ScienceDaily

July 3, 2018

New research unravels potential genetic mechanism behind this disparity.


Mechanism of catalysis[edit]. The molecular mechanism of
O-linked N-acetylglucosamine transferase

Researchers have recently begun to realize that biological sex plays a key role in disease risk. Sex plays a role in hypertension, diabetes, arthritis -- and in many neurological and psychiatric disorders.


Depression and anxiety affect females more, while neurodevelopmental disorders, including autism spectrum disorders, early onset schizophrenia, and attention deficit hyperactivity, affect more males. Males are also more sensitive to prenatal insults, such as gestational stress, maternal infection and drug exposure.

To better understand the molecular underpinnings of this disparity, Tracy Bale of the University of Maryland School of Medicine, along with several colleagues, focused on a molecule that plays a key role in placental health.

In a study of mice, they found that the molecule, O-linked N-acetylglucosamine transferase (OGT) works by establishing sex-specific patterns of gene expression.

The study was published this week in the journal Nature Communications.

OGT seems to work via an epigenetic modification that broadly controls transcription, H3K27me3. Epigenetics is the study of changes in how genes are expressed.

Dr. Bale showed that high levels of H3K27me3 in the female placenta produce resilience to stress experienced by the mother. This indicates at least one molecular pathway that allows females to be more resilient to maternal stress than males.

"This pathway could help explain why we see this profound neurodevelopmental difference in humans," said Dr. Bale. "OGT and H3K27me3 in the placenta are crucial to a lot of protein encoding that occurs during pregnancy, and so this process has a lot of downstream effects. The OGT gene is on the X chromosome, and seems to provide a level of protection for the female fetus to perturbations in the maternal environment."

Dr. Bale has focused much of her research on the links between stress and subsequent risk for neurodevelopmental disorders, including autism and schizophrenia in offspring. Her previous work on the placenta has found novel sex differences that may predict increased prenatal risk for disease in males.

She had previously found that, in mice, a father's stress can affect the brain development of offspring. Stress can alter the father's sperm, which can alter the brain development of the child.

Dr. Bale has also found that male mice experiencing chronic mild stress have offspring with a reduced hormonal response to stress; this response has been linked to some neuropsychiatric disorders, including PTSD.

This suggests that even mild environmental challenges can have a significant effect on the health of offspring.

Journal Reference
  • Bridget M. Nugent, Carly M. O’Donnell, C. Neill Epperson, Tracy L. Bale. Placental H3K27me3 establishes female resilience to prenatal insults. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-04992-1

Friday, July 13, 2018

Optimism Greets Investors’ Sudden Interest in Autism Therapy

From Spectrum News

By Hannah Furfaro
June 9, 2018


The Maryland-based autism therapy company Learn It Systems was a small operation with big ambitions when it caught the eye of a major private equity firm called LLR Partners.

At the time, in 2016, hundreds of small providers across the United States offered applied behavioral analysis (ABA), the most widely used autism treatment. But no big players had yet entered the fledgling market. Philadelphia-based LLR Partners bid to purchase Learn It Systems for an undisclosed amount.

Since then, Learn It Systems, now called Learn Behavioral, has increased the number of states in which it offers ABA services from 11 to 18 and more than quadrupled its workforce to 3,400 employees.

“There’s been a tremendous amount of expansion,” says Michael Maloney, the company’s president and chief executive officer.

In the past five years, more than a dozen private equity firms like LLR Partners have injected hundreds of millions of dollars into companies that offer autism treatment. Only one such deal was struck in 2009, but about a dozen were made in 2016, and there were 19 more in 2017.

Changes to U.S. federal and state laws now compel insurance companies to reimburse autism treatments — and have made the field attractive to investors. A dearth of major treatment centers for autism has also enticed investors.

Despite the encroachment of financial firms into this medical arena, many researchers are, in fact, cautiously optimistic — because the investments may give more families access to care they need.

“I think if it becomes part of a conglomerate that also sells turkey bacon and Ziploc bags, then that would probably be a problem,” says Catherine Lord, director of the Center for Autism and the Developing Brain at Weill Cornell Medicine in New York, who has served as a consultant to an investment firm. (Lord was paid $2,000 to consult for an investment company but declined to name the firm.)


“But if it actually means the business is expanded, or organized even more efficiently, I’m not sure that that’s a bad thing.”

Open Market

Before the change requiring insurance reimbursements, investors showed no interest in autism.

The autism market “virtually didn’t exist” a few years ago, says Dexter Braff, president of the Pittsburgh-based Braff Group, which advises companies on mergers and acquisitions. Once state and federal laws changed, ABA companies launched through clinicians’ grassroots efforts rapidly became profitable.

“Suddenly, businesses all over the country were growing into $10 million or $20 million of revenue,” says Michael Bernstein, a partner at Baird Capital, which last year purchased a stake in an Indiana-based ABA company called Hopebridge. “Those businesses suddenly became candidates for investment.”

ABA companies are coy about how much money they make, but those that offer autism therapy, overall, generate somewhere between $15 billion and $80 billion each year, Maloney says.


He bases this estimate on figures from the Centers for Disease Control and Prevention, which estimates that intensive behavioral therapies for autism cost up to $60,000 per child each year.

Some experts are worried about this influx, saying a focus on profit might increase costs for families and lower the quality of the treatment offered.

“It’s not that I think the companies are inherently bad or that they’re inherently flawed, but it’s all in the execution,” says Bennett Leventhal, professor of child and adolescent psychiatry at the University of California, San Francisco. (Leventhal has provided informal, unpaid consulting to some companies.) “If one looks at how venture capitalists have executed other things, it rarely reduces costs and rarely increases quality,” he says.


Two Cents

The care offered at certain ABA centers is already outdated or low quality, others say; offering mediocre care more widely does not serve families well.

“They have the potential of expanding an ineffective program,” says Lynn Koegel, clinical professor of psychiatry and behavioral sciences at Stanford University, in California.

Still, some researchers are willing to suspend judgment for now.

Kevin Pelphrey says he’s “cautiously optimistic” that bigger business will translate into more jobs for clinicians and more widely available care for families.

“In our economy, and with our model of medical care and treatment provisions, getting investors interested in it is really the best option we’ve got,” says Pelphrey, director of the Autism and Neurodevelopmental Disorders Institute at the George Washington University, in Washington, D.C.

Insurance companies help ensure that ABA providers are trained and that companies follow established treatment protocols. Investors are also calling on experts for advice on the best approaches.

“They were asking me, ‘Are these the most modern approaches? Is this really as scientifically grounded as the ABA group claimed? Are they really at the cutting edge of intervention?’” Lord says.

Still, she says, she cautioned officials at the investment company about the pitfalls they may face. Even if treatment centers are diligent about documenting services children receive, for instance, tracking the children’s improvement can be challenging. “That’s a limitation for all of us,” she says.

With Reunification, New Orleans Becomes the First District in the Country to Oversee a Citywide System of Public Charter Schools. Will It Work?

From The 74 Million

By Beth Hawkins
July 8, 2018

An iconic building with iron metal balconies in the French Quarter of New Orleans.

On July 1, the current chapter of New Orleans’s unprecedented and closely tracked school improvement effort ended, and another — arguably one with bigger potential lessons for other urban school districts — began.

For the first time since Hurricane Katrina, a local, elected school board now controls all but a handful of the city’s 86 public schools. And, since the majority of them have been converted to public charter schools since the hurricane struck in 2005, the Orleans Parish School Board is now the nation’s first district to oversee a citywide system of charters.

The inflection point, as the city’s education organizations have termed it, comes at a crucial time for both the city’s schools, which are struggling to break through an academic plateau, and policy watchers, who are anxious to see whether the first-of-its-kind system can confront long-standing equity issues in public education.

Under unification, virtually all New Orleans schools operate under a bargain cemented in state law. Schools will be run by their own boards, which will continue to decide how to staff and operate them.

But, as the schools’ charter authorizer, the district will hold them to a set of recently agreed-to standards and will be responsible for systemwide tasks such as transportation, enrollment, discipline, and finance.

Related

If successful, the distribution of power could be copied by other districts struggling with tensions between a community’s need for democratic influence over its school system and the schools’ need to be buffered from constantly changing political winds.


“The governance construct alone — that’s our biggest innovation,” says Patrick Dobard, who was superintendent of the state’s Recovery School District for five years and now heads the nonprofit New Schools for New Orleans. “This summer is one of the largest milestones in years.”

The new district is markedly leaner, with 44,000 students, down from 64,000 before the storm. Only 6 percent attend schools defined as failing, as opposed to two-thirds in 2005. The graduation rate has risen from 54 percent to 73 percent, and the percentage of graduates going to college has risen from 37 percent to 61 percent. Average ACT scores are up two points, to almost 19 out of 36 — within half a point of Louisiana’s average and two points below the national average.

The number of students passing state math and reading exams almost doubled between 2005 and 2014, to 62 percent. The percentage scoring in the highest tier, “mastery,” has risen from 8 percent to 25 percent.

In May, the Orleans Parish School Board adopted a framework for holding its schools to performance standards, the final major decision to be made before the state returns the last 38 schools under its control. District administration has shrunk radically, with 98 percent of funds going to schools, as opposed to 85 percent to 90 percent in most districts.

The system remains racially and economically isolated, however. While the number of white students has risen steadily, almost all public school students are impoverished, and 87 percent are black. One-fourth of school-age residents attend private schools, one of the highest rates in the nation.

And progress has slowed. After rising from mostly Es and Fs on state report cards to a collective C in 2014, achievement reached a plateau systemwide and in 2017 declined. The number of D and F schools — now serving some 20,000 students — is expected to increase for two reasons when scores are released in a few weeks: New, higher state standards are in place this year, and a curve the state had used to grade schools as standards rose will be eliminated.

Because the city needs to hire some 900 teachers each year, New Schools for New Orleans has brought together Xavier and Loyola universities, teachNOLA, TNTP, and the Relay Graduate School of Education to jointly train 900 educators by 2020.

Finally, individual schools and school networks are heeding outside pressure to adopt stronger curricula.

The challenges are huge, says Dobard, but the city has faced longer odds in recent years. When Katrina struck a few days into the 2005 school year, New Orleans was one of the lowest-performing districts in the country. Its 117 schools needed $1.5 billion in deferred maintenance.

“The problems,” says Dobard, “just seemed too daunting to try to solve.”

In the weeks after the storm, the Louisiana Legislature passed a law giving authority over most of the city’s schools to a Recovery School District, a state entity created two years before to take over and turn around chronically underperforming schools throughout the state. (The 10 selective-admissions schools that remained under control of the Orleans Parish School Board were academically high-performing.)

As the state district reopened schools, the percentage operating as public charter schools rose quickly. Results were uneven, and families struggled to negotiate the crippled infrastructure to find seats for their children. Special education services were notoriously sparse, and with no central office to stop them, some schools refused to admit students with challenges, or expelled them.

As schools were rebuilt or replaced using $1.8 billion in federal aid, student achievement in the best-run programs rose. The recovery district began revoking the charters of schools that couldn’t bring student performance up and giving them to successful charter school networks.

As the 10th anniversary of Katrina approached, though, academic progress flagged and New Orleans residents began protesting their lack of say in their own schools, which enrolled mostly black children but were largely overseen by white outsiders. In a move that flew under the radar until the eleventh hour, city education leaders pushed to enshrine the recovery district’s autonomy-for-accountability bargain in law.

Successful schools have had the ability to return to local control since 2010, but few made the switch. Some feared re-encountering the corruption and mismanagement that had dogged the elected board in the past. Some saw no advantage to rejoining the district.

But it was clear that a landscape comprising a handful of traditional district schools that could select their students and dozens of stand-alone charter schools was both chaotic and inequitable. Recovery district officials started meeting with school leaders to hammer out systems aimed at making enrollment, discipline, and funding fair.

Using an algorithm modeled on the software that matches medical students to residency programs, the district created a common enrollment system. Combined with a policy mandating district oversight of suspensions and expulsions, the central repository of attendance data forced all schools to take a more equitable share of students with disabilities and challenging behaviors.

The district also created a funding formula designed to offset the costs of educating children who need extra support. In addition to a basic per-pupil allotment, schools are now given supplemental funds for students learning English, gifted and talented students, and children with varying levels of disability.

In 2016, New Orleans lawmakers introduced a bill to return the city’s schools to the local district, which would be bound by the proposed law to continue the new systems and constrained from intervening in most school-level affairs.

“We are at a very unique place in time in that we are now able to start in earnest the transition of the governance of schools in New Orleans,” Dobard told Louisiana’s House Education Committee at the time.

“One of the reasons we’ve been able to be successful to a degree is that we have a very strong policymaking framework in New Orleans, one that emanates from schools, from school leaders, from those closest to the children who do the work every day.”

The measure carried, requiring the state to return its New Orleans schools to the Orleans Parish School Board by July 1, 2018. (Seven schools that are located in the city but are authorized by the state legislature and Board of Elementary and Secondary Education because of the populations they serve will remain outside the unified district.)

As the unification date approached, even the strongest proponents acknowledged doubts, topped by the fear that an elected board won’t be able to hold the line on revoking underperforming schools’ charters. Even if it can, some question whether, having made rapid progress up from rock bottom, schools will be able to surmount the challenges that most U.S. urban districts face.

Near the end of the current school year, Cypress Academy, a three-year-old school with a focus on serving children with dyslexia and other reading disabilities, abruptly announced it would not reopen in the fall. Its staffing model was not financially sustainable, board members said — a situation the New Orleans news site The Lens reported district leaders were aware of.

Following a backlash within the school community, the Orleans Parish School Board agreed to run the school for two years. Debate ensued over whether the district, now a charter school authorizer with uncertain ability or duty to intervene, should have communicated with Cypress families.

Time will tell whether the district’s novel structure will allow for both the continual problem-solving required in a large urban school system and the innovation necessary to create enough high-performing schools to serve every child in the city.

“This is work you have to stay committed to. This road is long,” says Dobard. “Victory would be 100 percent excellent schools.”

Thursday, July 12, 2018

Judge Allows Electric Shocks on Those with Disabilities to Continue

From DisabilityScoop

By Courtney Perkes
July 10, 2018

A Massachusetts judge is allowing the practice of shocking children and adults with disabilities to continue at a residential center even as federal regulators mull banning the controversial behavior modification technique.


Late last month, Judge Katherine Field of Bristol County Probate and Family Court ruled in favor of the Judge Rotenberg Educational Center in Canton, Mass., which is the only facility in the U.S. using electric shock devices to deter negative behavior in those with disabilities, according to court documents.

In the decision, Field wrote that state officials failed to demonstrate a professional consensus that the so-called aversive treatment “does not conform to the accepted standard of care for treating individuals with intellectual and developmental disabilities.”

The case, which was brought by the state of Massachusetts in 2013, sought to end a court order that has limited the state’s regulatory authority of the center since the 1980s. Officials argued unsuccessfully that the advent of newer psychotropic medications could be used to treat harmful behaviors instead, according to court documents.

The Rotenberg Center said in a statement that skin shock is used as a last resort for “severe self-abusive, aggressive and health-dangerous behavior disorders.”

“There is no more important issue in the world to the students and families who rely on this treatment and we are satisfied the court took the time to engage in a fact-based review to reach this ruling,” the statement said.

Massachusetts officials did not respond to a request for comment, however in an interview with WCVB-TV Boston, Health and Human Services Secretary Marylou Sudders said the department would consider whether to appeal.

The use of electric shock at the Rotenberg Center, which provides residential and day services, has raised concern for years from disability advocacy groups and federal regulators.

Samantha Crane, legal director and director of public policy for the Autistic Self Advocacy Network, said some people with autism process pain in such a way that a shock can feel like torture.

“Even people who aren’t on the autism spectrum who have experienced this shock say it’s extremely painful,” she said.

According to the court papers, the shocks can only be provided with consent from a parent or guardian and a judge. The shocks last two seconds and are mostly given to adults rather than children, the documents say. Staff administer the shocks via a battery operated transmission device worn on the arm or leg.

In 2016, the Food and Drug Administration proposed a ban on the devices, noting “significant psychological and physical risks are associated with the use of these devices.”

But final action hasn’t been taken. FDA spokeswoman Stephanie Caccomo said the agency is continuing to work on the issue and she couldn’t speculate on the timing of a decision.

“This is an issue that’s really galvanized the disability community,” said Crane of the Autistic Self Advocacy Network. “It’s got unprecedented community involvement. We’ve got a petition with over 290,000 signatures asking the FDA to finalize this ban.”

Department of Education Delays Equity in IDEA Compliance Date by Two Years

From Education Dive

By Amelia Harper
July 3, 2018


Dive Brief
  • The U.S. Department of Education (ED) is postponing until July 1, 2020 the date states need to comply with an Obama-era rule under the Individuals with Disabilities Education Act (IDEA) that would address whether minority students are disproportionately placed in special education. Secretary of Education Betsy DeVos's office has also announced postponing the date for including children ages 3 to 5 in the analysis of significant disproportionality from July 1, 2020 to July 1, 2022, according to a document released by the ED and published in the Federal Register today.
  • During the public comment period on the "equity in IDEA" rule, 390 parties submitted comments, with many commentators opposing the notion of postponement, arguing that the status quo is unacceptable, that states have failed to adequately solve the issue — which some regard as a civil rights problem — and that federal government needs to intervene.
  • However, the announcement says the ED does not feel that the causes and solutions for the problem have received sufficient study and believes “that the racial disparities in the identification, placement or discipline of children with disabilities are not necessarily evidence of, or primarily caused by, discrimination, as some research indicates.” The delay, the department says, will “also give states the opportunity to examine this issue through their own policies and procedures.”

Dive Insight

The news that ED is delaying compliance of the Obama-era special education rule does not come as a shock to most educators. DeVos and her team have been proposing the delay for months.

Some say the delay is the result of a directive issued by President Donald Trump which requires all federal agencies to identify regulations for “repeal, replacement or modification.” Some opponents of the decision claim that DeVos lacks understanding of the issues or is deliberately targeting minorities, while others say DeVos simply views these issues from a state-led school choice perspective.

Many frame the delay as a civil rights and equity issue. Reports, such as the one released in April by Harvard University, add fuel to that argument.


The report, entitled “Disabling Punishment," asserts that black students with disabilities miss significantly more class time because of suspensions than their white peers. As Catherine Kramarczuk Voulgarides, author of “Does Compliance Matter in Special Education? IDEA and the Hidden Inequities of Practice” said,

“We need to identify educational policy and practice remedies that seriously contend with the historical and social contexts that define the United States education system. If we don’t, we exhibit a concerning historical amnesia that denies the salience of race in American society and on educational outcomes.”

Whether the decision to delay implementation is a mistake or not remains to be seen. The ED defends the ruling, saying it will allow the administration to “ensure that we end up with a regulation that does address these issues appropriately.” The delay will allow time for more research and more voices to be heard.

In the meantime, while the delay will impact how schools respond to the required compliance regulations, there is no rule that prevents schools from actively setting goals to reduce overrepresentation of minority students in special education on their own.

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