Brenda Irwin’s Post

A study published on June 17, 2024, in Nature Medicine has used brain imaging and artificial intelligence to identify six distinct "biotypes" of depression and anxiety, potentially paving the way for more personalized treatments. Leanne Williams, PhD, director of the Stanford Medicine Center for Precision Mental Health and Wellness, highlighted the "immediate clinical implications" of this research, noting its potential to revolutionize treatment at a new precision mental health clinic at Stanford. Depression and anxiety are highly heterogeneous, with significant variability in symptoms and treatment responses among patients. Traditional approaches in psychiatry often result in a trial-and-error process, leaving 30-40% of patients without remission after one treatment attempt. The study involved 801 adults with depression and anxiety who underwent functional MRI (fMRI) to measure brain activity during rest and tasks designed to assess cognitive and emotional functions. Researchers examined six brain circuits linked to depression and used machine learning to identify six distinct biotypes based on specific patterns of brain dysfunction. These biotypes could predict responses to different treatments. For instance, patients with overactivity in cognitive brain regions responded best to the antidepressant venlafaxine, while those with heightened activity in three brain regions associated with problem-solving benefited more from behavioral therapy. Conversely, patients with lower activity in the attention control circuit at rest showed less improvement with behavioral therapy. The study's findings suggest a deeper understanding of the neurobiology of depression and anxiety, potentially improving diagnostic precision and treatment customization. However, naming the biotypes remains a "work in progress," with efforts underway to develop more descriptive medical terms for clinical use.

🧠Advancements and Challenges in Semantic Dementia Research: A Decade of Progress Semantic dementia (SD) has evolved significantly in the last decade, shedding light on its intricate nature beyond initial perceptions. Recent research utilizing advanced neuroimaging techniques has uncovered detailed insights into the semantic impairments and cognitive deficits associated with SD, highlighting affected brain networks both structurally and functionally. Pathological and genetic studies have further elucidated the diverse manifestations of SD, contributing to the observed variability in the disorder. Recent advancements have led to the identification of three distinct subtypes of SD based on atrophy lateralization, each progressing through three stages of severity. This refined classification aids in distinguishing SD from similar neurodegenerative conditions, enhancing diagnostic accuracy and treatment strategies for conditions like Alzheimer's disease (AD) and frontotemporal dementia. While progress has been made, significant challenges persist. Future research efforts must focus on identifying risk factors, improving early diagnosis, exploring potential subtypes, and developing effective treatments. A deeper understanding and more refined care approaches are crucial for advancing knowledge and enhancing outcomes for individuals affected by this rare neurodegenerative disorder. Article: https://lnkd.in/dbQ4aZzf #CognitiveNeuroscience #Neuroimaging #DementiaResearch

Early Detection of Alzheimer's Disease: The Crucial Role of Magnetic Resonance Imaging Early detection of Alzheimer’s disease (AD) is one of the most pressing challenges in modern neuroscience. Recently, a study published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging revealed that diffusion-weighted MRI can predict Alzheimer’s risk even before clinical symptoms appear, offering a valuable tool for preventive interventions. Microstructural Changes and Alzheimer's Risk Researchers have found that specialized MRI scans can detect early cortical microstructural changes in the brain that resemble patterns seen in AD pathology. This method can identify individuals at risk before significant cognitive decline occurs. Cortical microstructural changes, measured by mean diffusivity, were correlated with the deposition of tau, a protein associated with neurodegeneration. Individuals whose patterns of microstructural changes most closely resembled tau deposition exhibited greater memory decline over time. (MRI scan…) Importance of Early Diagnosis Early identification of markers of neurodegeneration offers a window of opportunity for interventions that can slow or even prevent the progression of the disease. The study emphasized that cortical mean diffusivity can detect subtle changes in the brain before substantial tissue loss occurs. This is crucial as it allows clinicians to initiate disease-modifying treatments earlier, potentially improving patient outcomes. (MRI scan…) Clinical Implications Diffusion-weighted magnetic resonance imaging (DWMRI) is widely available and noninvasive, making it a practical tool for population-based screening. Based on the spatial patterns of microstructural changes, clinicians can identify individuals at risk for Alzheimer’s disease in middle age, long before the onset of significant symptoms. This approach can be integrated into regular screening protocols for aging populations, facilitating the implementation of personalized preventive strategies. (DWMRI) Read in full at: https://lnkd.in/d7VKHsEj

📃Scientific paper: Differential responses toward conditioned and unconditioned stimuli, but decreased hypothalamic-pituitary-adrenal axis responsiveness in neonatal hippocampal lesioned monkeys Abstract: The hippocampus is important for long-term memory storage, but also plays a role in regulating the hypothalamic-pituitary-adrenal (HPA) axis and emotional behaviors. We previously reported that early hippocampal damage in monkeys result in increased anxious expression and blunted HPA responses to an acute stressor. Here, we further probe their responses toward aversive stimuli (conditioned and unconditioned) and evaluate HPA axis dysfunction. Responses toward social, innate, and learned aversive stimuli, fear potentiated acoustic startle, and pituitary-adrenal function were investigated in 13 adult rhesus monkeys with neonatal hippocampal lesions (Neo-Hibo=6) and controls (Neo-C=7). Neo-Hibo monkeys’ responses depend on the type of unconditioned stimulus, with increased anxiety behaviors toward social and learned, but decreased reactivity toward innate stimuli. Neo-C and Neo-Hibo monkeys exhibited similar performance learning conditioned cues and safety signals. Neo-Hibo monkeys were less sensitive to HPA axis stimulation, potentially suggesting adrenal fatigue. Current findings suggest that the hippocampus plays a large role in regulating not only anxiety behaviors, but also the HPA-axis, a neural system crucial to regulate how we respond to the world around us. These data have important clinical significance considering that many developmental neuropsychiatric disorders exhibit altered hippocampal structure and function, emotional and HPA axis dysregulation. Continued on ES/IODE ➡️ https://etcse.fr/tCp ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: Differential responses toward conditioned and unconditioned stimuli, but decreased hypothalamic-pituitary-adrenal axis responsiveness in neonatal hippocampal lesioned monkeys Abstract: The hippocampus is important for long-term memory storage, but also plays a role in regulating the hypothalamic-pituitary-adrenal (HPA) axis and emotional behaviors. We previously reported that early hippocampal damage in monkeys result in increased anxious expression and blunted HPA responses to an acute stressor. Here, we further probe their responses toward aversive stimuli (conditioned and unconditioned) and evaluate HPA axis dysfunction. Responses toward social, innate, and learned aversive stimuli, fear potentiated acoustic startle, and pituitary-adrenal function were investigated in 13 adult rhesus monkeys with neonatal hippocampal lesions (Neo-Hibo=6) and controls (Neo-C=7). Neo-Hibo monkeys’ responses depend on the type of unconditioned stimulus, with increased anxiety behaviors toward social and learned, but decreased reactivity toward innate stimuli. Neo-C and Neo-Hibo monkeys exhibited similar performance learning conditioned cues and safety signals. Neo-Hibo monkeys were less sensitive to HPA axis stimulation, potentially suggesting adrenal fatigue. Current findings suggest that the hippocampus plays a large role in regulating not only anxiety behaviors, but also the HPA-axis, a neural system crucial to regulate how we respond to the world around us. These data have important clinical significance considering that many developmental neuropsychiatric disorders exhibit altered hippocampal structure and function, emotional and HPA axis dysregulation. Continued on ES/IODE ➡️ https://etcse.fr/tCp ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Mayo Clinic scientists are leveraging AI and machine learning to enhance the analysis of electroencephalograms (EEGs), allowing neurologists to detect early signs of dementia more quickly and accurately. Traditionally used for epilepsy detection, EEGs monitor brain activity, with results often reviewed manually. However, the new AI-driven approach can identify subtle patterns linked to cognitive decline, such as Alzheimer's and Lewy body dementia, that might go unnoticed by human experts. The research, published in Brain Communications, analyzed data from over 11,000 EEGs performed at Mayo Clinic over a decade. AI models simplified complex brain wave patterns, enabling precise quantification of abnormalities. This technology has the potential to provide a more accessible, cost-effective tool for diagnosing cognitive issues, especially in underserved areas. While EEGs won’t replace other tests like MRIs or PET scans, they could complement them, offering earlier diagnosis and better treatment options for patients. #neurology #dementia #eeg With the Specialist combines artificial intelligence with a board certified neurologist to enhance neurological care in the primary care setting, bringing the neurology consult to you. #withthespecialist #aiinhealthcare #innovation https://lnkd.in/gQaXJFdX https://lnkd.in/gExrwg2P

Researchers from Boston University have developed a new tool powered by #generativeAI (genAI) that is capable of diagnosing 10 different types of #dementia, even if they co-occur. https://lnkd.in/d699iza3 #artificialintelligence #clinicalresearch #digitalhealth #genAI #healthtech #medicalaffairs #neurology

📃Scientific paper: Differential responses toward conditioned and unconditioned stimuli, but decreased hypothalamic-pituitary-adrenal axis responsiveness in neonatal hippocampal lesioned monkeys Abstract: The hippocampus is important for long-term memory storage, but also plays a role in regulating the hypothalamic-pituitary-adrenal (HPA) axis and emotional behaviors. We previously reported that early hippocampal damage in monkeys result in increased anxious expression and blunted HPA responses to an acute stressor. Here, we further probe their responses toward aversive stimuli (conditioned and unconditioned) and evaluate HPA axis dysfunction. Responses toward social, innate, and learned aversive stimuli, fear potentiated acoustic startle, and pituitary-adrenal function were investigated in 13 adult rhesus monkeys with neonatal hippocampal lesions (Neo-Hibo=6) and controls (Neo-C=7). Neo-Hibo monkeys’ responses depend on the type of unconditioned stimulus, with increased anxiety behaviors toward social and learned, but decreased reactivity toward innate stimuli. Neo-C and Neo-Hibo monkeys exhibited similar performance learning conditioned cues and safety signals. Neo-Hibo monkeys were less sensitive to HPA axis stimulation, potentially suggesting adrenal fatigue. Current findings suggest that the hippocampus plays a large role in regulating not only anxiety behaviors, but also the HPA-axis, a neural system crucial to regulate how we respond to the world around us. These data have important clinical significance considering that many developmental neuropsychiatric disorders exhibit altered hippocampal structure and function, emotional and HPA axis dysregulation. Continued on ES/IODE ➡️ https://etcse.fr/tCp ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Mayo Clinic scientists are using artificial intelligence (AI) and machine learning to analyze electroencephalogram (EEG) tests more quickly and precisely, enabling neurologists to find early signs of dementia among data that typically go unexamined. The century-old EEG, during which a dozen or more electrodes are stuck to the scalp to monitor brain activity, is often used to detect epilepsy. Its results are interpreted by neurologists and other experts trained to spot patterns among the test's squiggly waves. In new research published in Brain Communications, scientists at the Mayo Clinic Neurology AI Program (NAIP) demonstrate how AI can not only speed up analysis, but also alert experts reviewing the test results to abnormal patterns too subtle for humans to detect. The technology shows the potential to one day help doctors distinguish among causes of cognitive problems, such as Alzheimer's disease and Lewy body dementia. The research suggests that EEGs, which are more widely available, less expensive and less invasive than other tests to capture brain health, could be a more accessible tool to help doctors catch cognitive issues in patients early. "There's a lot of medical information in these brain waves about the health of the brain in the EEG," says senior author David T. Jones, M.D., a neurologist and director of NAIP. "It's well known that you can see these waves slow down and look a bit different in people who have cognitive problems. In our study, we wanted to know if we could accurately measure and quantify that type of slowing with the aid of AI." To develop the tool, researchers assembled data from more than 11,000 patients who received EEGs at Mayo Clinic over the course of a decade. They used machine learning and AI to simplify complex brain wave patterns into six specific features, teaching the model to automatically discard certain elements, such as data that should be ignored, in order to zero in on patterns characteristic of cognitive problems like Alzheimer's disease. Read more: https://lnkd.in/g2GB_J2Q Stay in touch with all the leading stories, events and opportunities by subscribing to my weekly LinkedIn Newsletter here:  https://bit.ly/3KQs0mD

AI data analysis maps three distinct brain aging patterns (National Institute on Aging (NIA), The National Institutes of Health) An AI-assisted analysis of extensive genetic, neurological, and cardiovascular data has revealed three distinct brain aging patterns and dementia risk outcomes, as detailed in a study published in JAMA Psychiatry. Conducted by an international team including NIA-funded researchers at the University of Pennsylvania, the study utilized over two decades of data from more than 27,000 participants. The team identified a normal aging trajectory (A1) and two accelerated aging subgroups (A2 and A3), particularly pronounced after age 65. The A1 group exhibited minor brain atrophy and fewer cardiovascular risk factors. The A2 group showed high levels of white matter hyperintensity and cardiovascular risk factors, with a brain age ahead of their chronological age by two to three years. The A3 group had widespread brain atrophy and faster cognitive decline, with a brain age three to five years older than their chronological age. These findings could enhance prediction and classification in Alzheimer’s research and precision medicine, with hopes of expanding the study to more diverse samples. Read more here: https://lnkd.in/g9hjGq7q Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals https://lnkd.in/gcwMbj8X #BrainAging #DementiaResearch #AI #AlzheimersDisease #CognitiveHealth #PrecisionMedicine #Neuroimaging #ArtificialIntelligence #Healthcare #Medical #Alzheimers #Health #Cognitive #Dementia #Brain #Research #NIA #NIH