Spatiotemporal whole-brain activity and functional connectivity of melodies recognition.

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

Artificial intelligence and increasing misinformation.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

Artificial intelligence and cybercrime: implications for individuals and the healthcare sector.

The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.

Implications of Online Self-Diagnosis in Psychiatry.

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

Telepsychiatry in an Era of Digital Mental Health Startups.

PURPOSE OF REVIEW: Telepsychiatry practiced by psychiatrists is evidence-based, regulated, private, and effective in diverse settings. The use of telemedicine has grown since the COVID-19 pandemic as people routinely obtain more healthcare services online. At the same time, there has been a rapid increase in the number of digital mental health startups that offer various services including online therapy and access to prescription medications. These digital mental health firms advertise directly to the consumer primarily through digital advertising. The purpose of this narrative review is to contrast traditional telepsychiatry and the digital mental health market related to online therapy. RECENT FINDINGS: In contrast to standard telepsychiatry, most of the digital mental health startups are unregulated, have unproven efficacy, and raise concerns related to self-diagnosis, self-medicating, and inappropriate prescribing. The role of digital mental health firms for people with serious mental illness has not been determined. There are inadequate privacy controls for the digital mental health firms, including for online therapy. We live in an age where there is widespread admiration for technology entrepreneurs and increasing emphasis on the role of the patient as a consumer. Yet, the business practices of digital mental health startups may compromise patient safety for profits. There is a need to address issues with the digital mental health startups and to educate patients about the differences between standard medical care and digital mental health products.

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review.

This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.

Longitudinal Digital Mood Charting in Bipolar Disorder: Experiences with ChronoRecord Over 20 Years.

INTRODUCTION: Longitudinal study is an essential methodology for understanding disease trajectories, treatment effects, symptom changes, and long-term outcomes of affective disorders. Daily self-charting of mood and other illness-related variables is a commonly recommended intervention. With the widespread acceptance of home computers in the early 2000s, automated tools were developed for patient mood charting, such as ChronoRecord, a software validated by patients with bipolar disorder. The purpose of this study was to summarize the daily mood, sleep, and medication data collected with ChronoRecord, and highlight some of the key research findings. Lessons learned from implementing a computerized tool for patient self-reporting are also discussed. METHODS: After a brief training session, ChronoRecord software for daily mood charting was installed on a home computer and used by 609 patients with affective disorders. RESULTS: The mean age of the patients was 40.3±11.8 years, a mean age of onset was 22±11.2 years, and 71.4% were female. Patients were euthymic for 70.8% of days, 15.1% had mild depression, 6.6% had severe depression, 6.6% had hypomania, and 0.8% had mania. Among all mood groups, 22.4% took 1-2 medications, 37.2% took 3-4 medications, 25.7 took 5-6 medications, 11.6% took 7-8 medications, and 3.1% took >8 medications. CONCLUSION: The daily mood charting tool is a useful tool for increasing patient involvement in their care, providing detailed patient data to the physician, and increasing understanding of the course of illness. Longitudinal data from patient mood charting was helpful in both clinical and research settings.

Dynamic coupling of whole-brain neuronal and neurotransmitter systems.

Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of whole-brain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific serotoninergic receptor (5-HT2AR) stimulation with psilocybin in healthy humans. This advance provides an understanding of why psilocybin is showing considerable promise as a therapeutic intervention for neuropsychiatric disorders including depression, anxiety, and addiction. Overall, these insights demonstrate that the whole-brain mutual coupling between the neuronal and the neurotransmission systems is essential for understanding the remarkable flexibility of human brain function despite having to rely on fixed anatomical connectivity.

Expectations for Artificial Intelligence (AI) in Psychiatry.

PURPOSE OF REVIEW: Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. RECENT FINDINGS: For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry.

PURPOSE OF REVIEW: Emotion artificial intelligence (AI) is technology for emotion detection and recognition. Emotion AI is expanding rapidly in commercial and government settings outside of medicine, and will increasingly become a routine part of daily life. The goal of this narrative review is to increase awareness both of the widespread use of emotion AI, and of the concerns with commercial use of emotion AI in relation to people with mental illness. RECENT FINDINGS: This paper discusses emotion AI fundamentals, a general overview of commercial emotion AI outside of medicine, and examples of the use of emotion AI in employee hiring and workplace monitoring. The successful re-integration of patients with mental illness into society must recognize the increasing commercial use of emotion AI. There are concerns that commercial use of emotion AI will increase stigma and discrimination, and have negative consequences in daily life for people with mental illness. Commercial emotion AI algorithm predictions about mental illness should not be treated as medical fact.

Increasing Cybercrime Since the Pandemic: Concerns for Psychiatry.

PURPOSE OF REVIEW: Since the pandemic, the daily activities of many people occur at home. People connect to the Internet for work, school, shopping, entertainment, and doctor visits, including psychiatrists. Concurrently, cybercrime has surged worldwide. This narrative review examines the changing use of technology, societal impacts of the pandemic, how cybercrime is evolving, individual vulnerabilities to cybercrime, and special concerns for those with mental illness. RECENT FINDINGS: Human factors are a central component of cybersecurity as individual behaviors, personality traits, online activities, and attitudes to technology impact vulnerability. Mental illness may increase vulnerability to cybercrime. The risks of cybercrime should be recognized as victims experience long-term psychological and financial consequences. Patients with mental illness may not be aware of the dangers of cybercrime, of risky online behaviors, or the measures to mitigate risk. Technology provides powerful tools for psychiatry but technology must be used with the appropriate safety measures. Psychiatrists should be aware of the potential aftermath of cybercrime on mental health, and the increased patient risk since the pandemic, including from online mental health services. As a first step to increase patient awareness of cybercrime, psychiatrists should provide a recommended list of trusted sources that educate consumers on cybersecurity.

Association between Adherence with an Atypical Antipsychotic and with Other Psychiatric Drugs in Patients with Bipolar Disorder.

BACKGROUND: Using U.S. pharmacy and medical claims, medication adherence patterns of patients with serious mental illness suggest that adherence to atypical antipsychotics may be related to adherence to other prescription drugs. This study investigated whether adherence to an atypical antipsychotic was related to adherence to other prescribed psychiatric drugs using self-reported data from patients with bipolar disorder. METHODS: Daily self-reported medication data were available from 123 patients with a diagnosis of bipolar disorder receiving treatment as usual who took at least 1 atypical antipsychotic over a 12-week period. Patients took a mean of 4.0±1.7 psychiatric drugs including the antipsychotic. The adherence rate for the atypical antipsychotic was compared to that for other psychiatric drugs to determine if the adherence rate for the atypical antipsychotic differed from that of the other psychiatric drug by at least ±10%. RESULTS: Of the 123 patients, 58 (47.2%) had an adherence rate for the atypical antipsychotic that differed from the adherence rate for at least 1 other psychiatric drug by at least±10%, and 65 (52.8%) patients had no difference in adherence rates. The patients with a difference took a larger total number of psychiatric drugs (p<0.001), had a larger daily pill burden (p=0.020) and a lower adherence rate with the atypical antipsychotic (p=0.007), and were more likely to take an antianxiety drug (p<0.001). CONCLUSION: Adherence with an atypical antipsychotic was not useful for estimating adherence to other psychiatric drugs in about half of the patients with bipolar disorder.

Adjunctive thyroid hormone treatment in rapid cycling bipolar disorder: A double-blind placebo-controlled trial of levothyroxine (L-T4 ) and triiodothyronine (T3 ).

OBJECTIVES: This report describes the first comparative double-blind, placebo-controlled trial of levothyroxine (L-T4 ) and triiodothyronine (T3 ) as adjunctive treatments in rapid cycling bipolar disorder. METHODS: Thirty-two treatment-resistant, rapid cycling patients who had failed a trial of lithium were randomized into three treatment arms: L-T4 , T3 , or placebo. They were followed for ≥4 months with weekly clinical and endocrine assessments. RESULTS: There were no statistically significant differences between the groups in age, gender, duration of illness, or thyroid status. Markov chain analyses were employed to assess treatment effects on cycling patterns among mood states (euthymia, depression, mania, and mixed). Within groups, post-treatment the L-T4 group spent significantly less time depressed or in a mixed state and greater time euthymic. The T3 and placebo groups did not differ significantly pre- and post-treatment in any mood state, although the pattern of effects was the same for the T3 group as for the L-T4 group. Between groups, the L-T4 group had a significantly greater increase in time euthymic and decrease in time in the mixed state than the placebo group. Other group differences were not significant, although they were in the expected direction. CONCLUSIONS: The findings in this first double-blind study directly comparing the effects of L-T4 and T3 therapy against placebo provide evidence for the benefit of adjunctive L-T4 in alleviating resistant depression, reducing time in mixed states and increasing time euthymic. Adjunctive T3 did not show statistically significant evidence of benefit over placebo in reducing the time spent in disturbed mood states.

Gender differences in thyroid system function: relevance to bipolar disorder and its treatment.

OBJECTIVES: Thyroid hormones play a critical role in the functioning of the adult brain, and thyroid diseases impair both mood and cognition. This paper reviews gender differences in thyroid system function that are relevant to the diagnosis and treatment of bipolar disorder. METHODS: The study comprised a comprehensive literature review of gender differences in thyroid disease that are pertinent to mood disorders. RESULTS: The prevalence of thyroid disease was found to be much higher in females than males, and to increase with age. The most commonly detected abnormality was subclinical hypothyroidism, which was found to occur in up to 20% of postmenopausal women. Females also had higher rates of thyroid autoimmunity. Individuals at risk for thyroid disease, such as adult females, may have had less ability to compensate for additional challenges to thyroid metabolism, including lithium treatment. Thyroid abnormalities were associated with a poorer response to standard treatments for mood disorders. Females with treatment-resistant mood disorders may have responded better than males to adjunctive therapy with thyroid hormones. CONCLUSIONS: Disturbances of thyroid system function, which occur commonly in females, may complicate the diagnosis and treatment of mood disorders. In particular, this is clinically relevant during lithium treatment because lithium may impair vital thyroid metabolic pathways secondary to its anti-thyroid activity.

One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics.

Surviving and thriving in a complex world require intricate balancing of higher order brain functions with essential survival-related behaviours. Exactly how this is achieved is not fully understood but a large body of work has shown that different regions in the prefrontal cortex (PFC) play key roles for diverse cognitive and emotional tasks including emotion, control, response inhibition, mental set shifting and working memory. We hypothesised that the key regions are hierarchically organised and we developed a framework for discovering the driving brain regions at the top of the hierarchy, responsible for steering the brain dynamics of higher brain function. We fitted a time-dependent whole-brain model to the neuroimaging data from large-scale Human Connectome Project with over 1000 participants and computed the entropy production for rest and seven tasks (covering the main domains of cognition). This thermodynamics framework allowed us to identify the main common, unifying drivers steering the orchestration of brain dynamics during difficult tasks; located in key regions of the PFC (inferior frontal gyrus, lateral orbitofrontal cortex, rostral and caudal frontal cortex and rostral anterior cingulate cortex). Selectively lesioning these regions in the whole-brain model demonstrated their causal mechanistic importance. Overall, this shows the existence of a 'ring' of specific PFC regions ruling over the orchestration of higher brain function.

Impact of sunlight on the age of onset of bipolar disorder.

OBJECTIVE: Although bipolar disorder has high heritability, the onset occurs during several decades of life, suggesting that social and environmental factors may have considerable influence on disease onset. This study examined the association between the age of onset and sunlight at the location of onset. METHOD: Data were obtained from 2414 patients with a diagnosis of bipolar I disorder, according to DSM-IV criteria. Data were collected at 24 sites in 13 countries spanning latitudes 6.3 to 63.4 degrees from the equator, including data from both hemispheres. The age of onset and location of onset were obtained retrospectively, from patient records and/or direct interviews. Solar insolation data, or the amount of electromagnetic energy striking the surface of the earth, were obtained from the NASA Surface Meteorology and Solar Energy (SSE) database for each location of onset. RESULTS: The larger the maximum monthly increase in solar insolation at the location of onset, the younger the age of onset (coefficient= -4.724, 95% CI: -8.124 to -1.323, p=0.006), controlling for each country's median age. The maximum monthly increase in solar insolation occurred in springtime. No relationships were found between the age of onset and latitude, yearly total solar insolation, and the maximum monthly decrease in solar insolation. The largest maximum monthly increases in solar insolation occurred in diverse environments, including Norway, arid areas in California, and Chile. CONCLUSION: The large maximum monthly increase in sunlight in springtime may have an important influence on the onset of bipolar disorder.

Brief depressive symptoms in patients with bipolar disorder: analysis of long-term self-reported data.

OBJECTIVE: Most patients with bipolar disorder experience depressive symptoms outside of an episode of depression as defined by DSM-IV criteria. This study explores the frequency of brief depressive episodes, lasting 1 to 4 days, using daily self-reported mood ratings. METHOD: Mood ratings were obtained from 448 patients (281 bipolar I, 167 bipolar II) using ChronoRecord software (91,786 total days). Episodes of depression and days of depression outside of episodes were determined. The intensity of depressive symptoms (mild versus moderate to severe) was compared. RESULTS: Using the DSM-IV length criteria, 61% of all depressive days occurred outside of a depressed episode. Decreasing the minimum length criterion to 2 days, both the number of patients experiencing a depressed episode (128 to 317) and the mean percent of days spent in a depressed episode by each patient (7.9% to 17.8.%) increased by about 2½ times, and 34.3% of depressed days remained outside of an episode. Depending on the episode length, the proportion of days within an episode with severe symptoms varied from 1/3 to 1/4 for episodes lasting from 14 to 2 days, and 1/4 for single-day episodes. There was no significant difference in the frequency of brief depressive episodes between bipolar I and II disorders. For all episode lengths, patients taking antidepressants spent 4% more days within an episode and 6% more days with depressive symptoms outside of an episode than those not taking antidepressants. CONCLUSION: Brief depressive episodes lasting 1 to 4 days occur frequently in bipolar disorder and do not distinguish between bipolar I and II disorders. Symptoms of moderate to severe intensity occur on 1/4 to 1/3 of the days in brief depressive episodes. This study did not address brief depression in those without bipolar disorder. Patients taking antidepressants experienced more brief depressive episodes. Controlled trials are needed to assess the impact of antidepressants on subsyndromal depressive symptoms.

Decreasing the minimum length criterion for an episode of hypomania: evaluation using self-reported data from patients with bipolar disorder.

Brief hypomania lasting less than 4 days may impair functioning and help to detect bipolarity. This study analyzed brief hypomania that occurred in patients with bipolar disorder who were diagnosed according to the DSM-IV criteria. Daily self-reported mood ratings were obtained from 393 patients (247 bipolar I and 146 bipolar II) for 6 months (75,284 days of data, mean 191.6 days). Episodes of hypomania were calculated using a 4, 3, 2, and single day length criterion. Brief hypomania occurred frequently. With a decrease in the minimum criterion from 4 days to 2 days, there were almost twice as many patients with an episode of hypomania (102 vs. 190), and more than twice as many episodes (305 vs. 863). Single days of hypomania were experienced by 271 (69%) of the sample. With a 2-day episode length, 33% of all hypomania remained outside of an episode. There was no significant difference in the percent of hypomanic days outside of an episode between patients with bipolar I and II disorders. There were no significant differences in the demographic characteristics of patients who met the 4-day minimum as compared with those who only experienced episodes of hypomania using a shortened length criterion. Decreasing the minimum length criterion for an episode of hypomania will cause a large increase in the number of patients who experience an episode and in the aggregate number of episodes, but will not distinguish subgroups within a sample who meet the DSM-IV criteria for bipolar disorder. Frequency may be an important dimensional aspect of brief hypomania. Clinicians should regularly probe for brief hypomania.

Association between median family income and self-reported mood symptoms in bipolar disorder.

OBJECTIVE: There is broad consensus from epidemiologic research that lower socioeconomic status is related to poorer health. This study investigated the relation between median family income and self-reported mood symptoms in patients with bipolar disorder who reside in the United States. METHODS: Two hundred eighty-four patients with bipolar disorder provided daily self-reported mood ratings for 6 months (50,054 days of data). Regardless of income, all patients were treated by a psychiatrist, took psychotropic medications, and participated in computerized self-monitoring throughout the study. Median family income was obtained from US census tract data. The association between income and mood was analyzed using income as both a continuous and categorical variable. Demographic characteristics were compared by income group. Education level was included in the analysis a priori. RESULTS: Both the continuous and categorical approaches found a positive association between income and euthymia, a negative association between income and manic/hypomanic symptoms including those due to mixed states, and no association between income and depressive symptoms. Patients in the lower-income group spent 12.4% fewer days euthymic than those in the upper-income group and 9.7% fewer days euthymic than those in the middle-income group. Patients in the lower-income group spent 7.1% more days with manic/hypomanic symptoms than those in the upper-income group. There was no association between education and income. CONCLUSION: Median family income is associated with mood symptoms in patients with bipolar disorder. Inclusion of income as a measure of socioeconomic status is recommended for future studies of outcome in bipolar disorder.

Internet of things issues related to psychiatry.

BACKGROUND: Internet of Things (IoT) devices for remote monitoring, diagnosis, and treatment are widely viewed as an important future direction for medicine, including for bipolar disorder and other mental illness. The number of smart, connected devices is expanding rapidly. IoT devices are being introduced in all aspects of everyday life, including devices in the home and wearables on the body. IoT devices are increasingly used in psychiatric research, and in the future may help to detect emotional reactions, mood states, stress, and cognitive abilities. This narrative review discusses some of the important fundamental issues related to the rapid growth of IoT devices. MAIN BODY: Articles were searched between December 2019 and February 2020. Topics discussed include background on the growth of IoT, the security, safety and privacy issues related to IoT devices, and the new roles in the IoT economy for manufacturers, patients, and healthcare organizations. CONCLUSIONS: The use of IoT devices will increase throughout psychiatry. The scale, complexity and passive nature of data collection with IoT devices presents unique challenges related to security, privacy and personal safety. While the IoT offers many potential benefits, there are risks associated with IoT devices, and from the connectivity between patients, healthcare providers, and device makers. Security, privacy and personal safety issues related to IoT devices are changing the roles of manufacturers, patients, physicians and healthcare IT organizations. Effective and safe use of IoT devices in psychiatry requires an understanding of these changes.