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  • richardmitnick 5:21 pm on December 4, 2017 Permalink | Reply
    Tags: , Depression and anxiety are the leading cause of disability and lost productivity worldwide with only one-third of patients recovering from treatment, Five new categories of mental illness have been identified by researchers in a Stanford-led study, Many different types of anxiety and depression exist new study finds, , Psychiatry, , Tension-anxious arousal- general anxiety- anhedonia - the inability to feel pleasure - and melancholia, The researchers collected and processed data from 420 participants both with healthy diagnoses and with multiple anxiety and depression diagnoses, The same tests were conducted with a second independent sample of 381 people   

    From Stanford Scope blog: “Many different types of anxiety and depression exist, new study finds” 

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    Stanford Scope blog

    December 4, 2017
    Tracie White

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    Photo by Sodanie Chea

    Five new categories of mental illness that cut across the current more broad diagnoses of anxiety and depression have been identified by researchers in a Stanford-led study.

    The five categories, defined by their specific symptoms and areas of brain activation, are: tension, anxious arousal, general anxiety, anhedonia — the inability to feel pleasure — and melancholia.

    “We are trying to disentangle the symptom overlap in our current diagnoses which can ultimately guide tailored treatment choices,” the researchers wrote in their study, which was published in JAMA Psychiatry.

    The research is part of an ongoing effort by Leanne Williams, PhD, professor of psychiatry and behavioral sciences and senior author of the study, and her lab, along with other groups within the field of psychiatric neuroscience, to better define mental illness in order to provide improved treatment plans for the millions of Americans who suffer from these disorders.

    Currently, depression and anxiety are the leading cause of disability and lost productivity worldwide with only one-third of patients recovering from treatment, the study said.

    The broad diagnostic categories as defined by the Diagnostic and Statistical Manual of Mental Disorders, such as anxiety and depression, have so many overlapping symptoms that it’s difficult to identify biological markers for potential treatments or cures, the researchers explained.

    “Currently, the treatments would be the same for anyone in these broad categories,” Williams said. “By refining the diagnosis, better treatment options could be prescribed, specifically for that type of anxiety or depression.”

    For their work, the researchers collected and processed data from 420 participants both with healthy diagnoses and with multiple anxiety and depression diagnoses. The participants underwent a series of tests involving brain mapping, self reporting of symptoms, and psychiatric diagnostic testing. Researchers measured how well participants functioned in everyday life, their capacity for building social relationships and general outlook on life.

    The same tests were conducted with a second independent sample of 381 people. Using a data-driven approach that involved machine learning algorithms, researchers processed the data and were able to identify the same five new categories across both groups.

    Results showed that 13 percent of participants were characterized by anxious arousal, 9 percent by general anxiety, 7 percent by anhedonia, 9 percent by melancholia and 19 percent by tension.

    “Interestingly, we found that many people who did not meet diagnostic criteria, but were still experiencing some symptoms, fell into the tension type,” said Katherine Grisanzio, lead author of the study and research lab manager in Williams’ lab.

    In the paper, the researchers further described the new categories:

    Tension: This type is defined by irritability. People are overly sensitive, touchy, and overwhelmed. The anxiety makes the nervous system hypersensitive.
    Anxious arousal: Cognitive functioning, such as the ability to concentrate and control thoughts, is impaired. Physical symptoms include a racing heart, sweating, and feeling stressed. “People say things like ‘I feel like I’m loosing my mind,” Williams said. “They can’t remember from one moment to the next.”
    Melancholia: People experience problems with social functioning. Restricted social interactions further cause distress.
    Anhedonia: The primary symptom is an inability to feel pleasure. This type of depression often goes unrecognized. People are often able to function reasonably well while in a high state of distress. “We see it in how the brain functions in overdrive,” Williams said. “People are able to power through but at some time become quite numb. These are some of the most distressed people.”
    General anxiety: A generalized type of anxiety with the primary features involving worry and anxious arousal — a more physical type of stress.

    See the full article here .

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    Scope is an award-winning blog founded in 2009 and produced by the Stanford University School of Medicine. If you’re curious about the latest advances in medicine and health and enjoy compelling, fresh and easily digestible news and features, then we’ve got just the thing. We’ve written quite a bit (7,000 posts and counting!), and we’re quite proud of it — so please enjoy.

    Leland and Jane Stanford founded the University to “promote the public welfare by exercising an influence on behalf of humanity and civilization.” Stanford opened its doors in 1891, and more than a century later, it remains dedicated to finding solutions to the great challenges of the day and to preparing our students for leadership in today’s complex world. Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto. Since 1952, more than 54 Stanford faculty, staff, and alumni have won the Nobel Prize, including 19 current faculty members

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  • richardmitnick 11:25 am on September 2, 2016 Permalink | Reply
    Tags: , , , Psychiatry,   

    From The Atlantic: “How Artificial Intelligence Could Help Diagnose Mental Disorders” 

    Atlantic Magazine

    The Atlantic Magazine

    Aug 23, 2016
    Joseph Frankel

    People convey meaning by what they say as well as how they say it: Tone, word choice, and the length of a phrase are all crucial cues to understanding what’s going on in someone’s mind. When a psychiatrist or psychologist examines a person, they listen for these signals to get a sense of their wellbeing, drawing on past experience to guide their judgment. Researchers are now applying that same approach, with the help of machine learning, to diagnose people with mental disorders.

    In 2015, a team of researchers developed an AI model that correctly predicted [Nature Partner Journal] which members of a group of young people would develop psychosis—a major feature of schizophrenia—by analyzing transcripts of their speech. This model focused on tell-tale verbal tics of psychosis: short sentences, confusing, frequent use of words like “this,” “that,” and “a,” as well as a muddled sense of meaning from one sentence to the next.

    Now, Jim Schwoebel, an engineer and CEO of NeuroLex Diagnostics, wants to build on that work to make a tool for primary-care doctors to screen their patients for schizophrenia. NeuroLex’s product would take a recording from a patient during the appointment via a smartphone or other device (Schwoebel has a prototype Amazon Alexa app) mounted out of sight on a nearby wall.

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    Adriane Ohanesian / Reuters

    Using the same model from the psychosis paper, the product would then search a transcript of the patient’s speech for linguistic clues. The AI would present its findings as a number—like a blood-pressure reading—that a psychiatrist could take into account when making a diagnosis. And as the algorithm is “trained” on more and more patients, that reading could better reflect a patient’s state of mind.

    In addition to the schizophrenia screener, an idea that earned Schwoebel an award from the American Psychiatric Association, NeuroLex is hoping to develop a tool for psychiatric patients who are already being treated in hospitals. Rather than trying to help diagnose a mental disorder from a single sample, the AI would examine a patient’s speech over time to track their progress.

    For Schwoebel, this work is personal: he thinks this approach may help solve problems his older brother faced in seeking treatment for schizophrenia. Before his first psychotic break, Schwoebel’s brother would send short, one-word responses, or make cryptic to references to going “there” or “here”—worrisome abnormalities that “all made sense” after his brother’s first psychotic episode, he said.

    According to Schwoebel, it took over 10 primary-care appointments before his brother was referred to a psychiatrist and eventually received a diagnosis. After that, he was put on one medication that didn’t work for him, and then another. In the years it took to get Schwoebel’s brother diagnosed and on an effective regimen, he experienced three psychotic breaks. For cases that call for medication, this led Schwoebel to wonder how to get a person on the right prescription, and at the right dose, faster.

    To find out, NeuroLex is planning a “pre-post study” on people who’ve been hospitalized for mental disorders “to see how their speech patterns change during a psychotic stay or a depressive stay in a hospital.” Ideally, the AI would analyze sample recordings from a person under a mental health provider’s care “to see which drugs are working the best” in order “to reduce the time in the hospital,” Schwoebel said.

    If a person’s speech shows fewer signs of depression or bipolar disorder after being given one medication, this tool could help show that it’s working. If there are no changes, the AI might suggest trying another medication sooner, sparing the patient undue suffering. And, once it’s gathered enough data, it could recommend a medication based on what worked for other people with similar speech profiles. Automated approaches to diagnosis have been anticipated in the greater field of medicine for decades: one company claims that its algorithm recognizes lung cancer with 50 percent more accuracy than human radiologists.

    The possibility of bolstering a mental health clinician’s judgment with a more “objective,” “quantitative” assessment appeals to the Massachusetts General Hospital psychiatrist Arshya Vahabzadeh, who has served as a mentor for a start-up accelerator Schwoebel cofounded. “Schizophrenia refers to a cluster of observable or elicitable symptoms” rather than a catchall diagnosis, he said. With a large enough data set, an AI might be able to split diagnoses like schizophrenia into sharper, more helpful categories based off the common patterns it perceives among patients. “I think the data will help us subtype some of these conditions in ways we couldn’t do before.”

    As with any medical intervention, AI aids “have to be researched and validated. That’s my big kind of asterisk,” he said, echoing a sentiment I heard from Schwoebel. And while the psychosis predictor study demonstrates that speech analysis can predict psychosis reasonably well, it’s still just one study. And no one has yet published a proof-of-concept for depression or bipolar disorder.

    Machine learning is a hot field, but it still has a ways to go—both in and outside of medicine. To take one example, Siri has struggled for years to handle questions and commands from Scottish users. For mental health care, small errors like these could be catastrophic. “If you tell me that a piece of technology is wrong 20 percent of the time”—or 80 percent accurate—“I’m not going to want to deploy it to a patient,” Vahabzadeh said.

    This risk becomes more disturbing when considering age, gender, ethnicity, race, or region. If an AI is trained on speech samples that are all from one demographic group, normal samples outside that group might result in false positives.

    “If you’re from a certain culture, you might speak softer and at a lower pitch,” which an AI “might interpret as depression when it’s not,” Schwoebel said.

    Still, Vahabzadeh believes technology like this could someday help clinicians treat more people, and treat them more efficiently. And that could be crucial, given the shortage of mental-health-care providers throughout the U.S., he says. “If humans aren’t going to be the cost-effective solution, we have to leverage tech in some way to extend and augment physicians’ reach.”

    See the full article here .

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