A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI.

Recent work demonstrating low test-retest reliability of neural activation during fMRI tasks raises questions about the utility of task-based fMRI for the study of individual variation in brain function. Two possible sources of the instability in task-based BOLD signal over time are noise or measurement error in the instrument, and meaningful variation across time within-individuals in the construct itself-brain activation elicited during fMRI tasks. Examining the contribution of these two sources of test-retest unreliability in task-evoked brain activity has far-reaching implications for cognitive neuroscience. If test-retest reliability largely reflects measurement error, it suggests that task-based fMRI has little utility in the study of either inter- or intra-individual differences. On the other hand, if task-evoked BOLD signal varies meaningfully over time, it would suggest that this tool may yet be well suited to studying intraindividual variation. We parse these sources of variance in BOLD signal in response to emotional cues over time and within-individuals in a longitudinal sample with 10 monthly fMRI scans. Test-retest reliability was low, reflecting a lack of stability in between-person differences across scans. In contrast, within-person, within-session internal consistency of the BOLD signal was higher, and within-person fluctuations across sessions explained almost half the variance in voxel-level neural responses. Additionally, monthly fluctuations in neural response to emotional cues were associated with intraindividual variation in mood, sleep, and exposure to stressors. Rather than reflecting trait-like differences across people, neural responses to emotional cues may be more reflective of intraindividual variation over time. These patterns suggest that task-based fMRI may be able to contribute to the study of individual variation in brain function if more attention is given to within-individual variation approaches, psychometrics-beginning with improving reliability beyond the modest estimates observed here, and the validity of task fMRI beyond the suggestive associations reported here.

A Web-Based Educational Module Using Clinical Neuroscience to Deliver the Diagnosis of Functional Neurological Disorder.

Neuroscience-based patient education has become an evidence-based strategy for enhancing chronic pain treatment. Advances in understanding the neuroscience of functional neurological disorder (FND) may allow similar approaches to be developed and disseminated to clinicians, given the public health need for greater provider awareness and expertise around the condition. Accordingly, the authors developed an online video module for clinicians that delivers neuroscience-based psychoeducation for FND and assessed whether the intervention would be associated with changes in clinicians' perception of FND patients and knowledge about the condition. The online intervention consisted of a 20-minute video module, including an 8-minute scripted role-play that modeled neuroscience-informed diagnosis delivery. Pre- and postintervention questionnaires were embedded into the online module and included a self-assessment of FND-related perceptions and knowledge and a multiple-choice assessment of retention of the neuroscience-based content. Wilcoxon signed-rank tests and McNemar's tests were used for statistical analyses. Of the 103 individuals who submitted surveys, 40 participants provided a complete data set from before and after the intervention. Following the intervention, self-assessment items showed respondents had significantly greater comfort with diagnosis delivery and treatment options and decreased negative perception of FND patients. The percentage of correct responses on a multiple-choice assessment regarding the functional neuroanatomy of FND was significantly increased. In summary, the online neuroscience-based educational intervention was effective for increasing clinician knowledge about FND and comfort with diagnosis delivery and treatment options. Implementing web-based formats may be a viable and cost-effective approach to disseminating knowledge and basic clinical skills in the care of patients with FND.

Neural correlates of inattention in adults with ADHD.

In the last two decades, numerous magnetic resonance imaging (MRI) studies have examined differences in cortical structure between individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) and healthy controls. These studies primarily emphasized alterations in gray matter volume (GMV) and cortical thickness (CT). Still, the scientific literature is notably scarce in regard to investigating associations of cortical structure with ADHD psychopathology, specifically inattention within adults with ADHD. The present study aimed to elucidate neurobiological underpinnings of inattention beyond GMV and CT by including cortical gyrification, sulcal depth, and fractal dimension. Building upon the Comparison of Methylphenidate and Psychotherapy in Adult ADHD Study (COMPAS), cortical structure parameters were investigated using 141 T1-weighted anatomical scans of adult patients with ADHD. All brain structural analyses were performed using the threshold-free cluster enhancement (TFCE) approach and the Computational Anatomy Toolbox (CAT12) integrated into the Statistical Parametric Mapping Software (Matlab Version R2021a). Results revealed significant correlations of inattention in multiple brain regions. Cortical gyrification was negatively correlated, whereas cortical thickness and fractal dimension were positively associated with inattention. The clusters showed widespread distribution across the cerebral cortex, with both hemispheres affected. The cortical regions most prominently affected included the precuneus, para-, pre-, and postcentral gyri, superior parietal lobe, and posterior cingulate cortex. This study highlights the importance of cortical alterations in attentional processes in adults with ADHD. Further research in this area is warranted to elucidate intricacies of inattention in adults with ADHD to potentially enhance diagnostic accuracy and inform personalized treatment strategies.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals.

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R2 scores between 0.60-0.74. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus.

The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the "big data" era, increasing computing power even on mobile devices, and online training resources have both led to an explosion in applications and publications of ML in the clinical neurosciences, but has also enabled a dangerous amount of flawed analyses and cardinal methodological errors committed by benevolent authors. While powerful ML methods are nowadays available to almost anyone and can be applied after just few minutes of familiarizing oneself with these methods, that does not imply that one has mastered these techniques. This textbook for clinicians aims to demystify ML by illustrating its methodological foundations, as well as some specific applications throughout clinical neuroscience, and its limitations. While our mind can recognize, abstract, and deal with the many uncertainties in clinical practice, algorithms cannot. Algorithms must remain tools of our own mind, tools that we should be able to master, control, and apply to our advantage in an adjunctive manner. Our hope is that this book inspires and instructs physician-scientists to continue to develop the seeds that have been planted for machine intelligence in clinical neuroscience, not forgetting their inherent limitations.

Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.

Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.g., XLNet, BERT, T5, and RoBERTa) and transfer learning. The objectives of this study were to (1) systematically review NLP applications in the clinical neurosciences, and (2) explore NLP analysis to facilitate literature synthesis, providing clear examples to demonstrate the potential capabilities of these technologies for a clinical audience. Our NLP analysis consisted of keyword identification, text summarization and document classification. A total of 48 articles met inclusion criteria. NLP has been applied in the clinical neurosciences to facilitate literature synthesis, data extraction, patient identification, automated clinical reporting and outcome prediction. The number of publications applying NLP has increased rapidly over the past five years. Document classifiers trained to differentiate included and excluded articles demonstrated moderate performance (XLNet AUC = 0.66, BERT AUC = 0.59, RoBERTa AUC = 0.62). The T5 transformer model generated acceptable abstract summaries. The application of NLP has the potential to enhance research and practice in the clinical neurosciences.

The Neurology and Neurosurgery Interest Group (NANSIG)-ten years of cultivating interest in clinical neurosciences.

Collaboration and successful teamworking are important components of clinical practise, and these skills should be cultivated early in medical school. The breadth of current medical school curricula means that students often have limited exposure to clinical neurosciences. Since its inception in 2009, the Neurology and Neurosurgery Interest Group (NANSIG) has become a national (UK and Republic of Ireland) example of student and junior doctor synergistic collaboration to deliver educational materials, research, conferences, seminars and workshops, as well as advocating for diversity in this field. Recently, it has expanded to incorporate an international audience and cater for a larger group of young medical professionals. The organisation has overcome numerous challenges and is constantly innovating new approaches to harness the necessary knowledge, skills and network to succeed in a career in neurosciences, neurology and neurosurgery. This article summarises the initiatives undertaken by the group over its first 10 years of existence and its organisational structure, as well as its future plans.

Clinical Neuroscience of Addiction: What Clinical Psychologists Need to Know and Why.

The last three decades in psychological research have been marked by interdisciplinary science. Addiction represents a prime example of a disorder marked by a complex interaction among psychosocial and biological factors. This review highlights critical findings in the basic neuroscience of addiction and translates them into clinical language that can inform clinical psychologists in their research, teaching, and practice. From mechanisms of reward processing, learning and memory, allostasis, incentive-sensitization, withdrawal, tolerance, goal-directed decision making, habit learning, genetics, inflammation, and the microbiome, the common theme of this review is to illustrate the clinical utility of basic neuroscience research and to identify opportunities for clinical science. The thoughtful integration of basic and clinical science provides a powerful tool to fulfill the scientific mission of improving health care. Clinical psychologists have a crucial role to play in the translational science of addiction.

Cyberbiosecurity: An Emerging Field that has Ethical Implications for Clinical Neuroscience.

Cyberbiosecurity is an emerging field that relates to the intersection of cybersecurity and the clinical and research practice in the biosciences. Beyond the concerns that usually arise in the areas of genomics, this paper highlights ethical concerns raised by cyberbiosecurity in clinical neuroscience. These concerns relate not only to the privacy of the data collected by imaging devices, but also the concern that patients using various stimulatory devices can be harmed by a hacker who either obfuscates the outputs or who interferes with the stimulatory process. The paper offers some suggestions as to how to rectify these increasingly dire concerns.

Pediatric onset multiple sclerosis.

Multiple Sclerosis (MS) is the commonest among inflammatory demyelinating diseases. While the disease prevalence is high in adults, frequency of pediatric onset multiple sclerosis (POMS) is very low in children and particularities in this population have been identified. We will address in this review characteristics of POMS.

Primary Literature In Clinical Neuroscience for In-Person Or Remote Instruction.

Structure and function relationships in the nervous system are a major component of neuroscience education. Readings and/or discussion of lesion studies in animal models are often used to demonstrate how brain injury/damage affects specific behaviors or cognitive processes. In contrast, primary literature in clinical neuroscience is less often used to teach brain structure and function relationships and this literature often describes remarkable stories of preserved brain function despite major brain injury/lesion. Here we describe a series of published articles in clinical neuroscience that we used in an undergraduate neuroscience course that challenge the simplistic views of brain localization of function and demonstrate the dynamic and plastic properties of the brain. Discussion of these primary articles can take place in-person or remote via video conferencing platforms.

Advancing gene therapies, methods, and technologies for Parkinson's Disease and other neurological disorders.

INTRODUCTION: Vector-based intracerebral gene therapies are being used to treat specific neurodegenerative conditions such as Parkinson's Disease (PD). This review presents a basis for central nervous system (CNS) gene therapy treatments of neurodegenerative diseases such as PD, as well as the need for novel skill sets and health delivery strategies within the clinical neurosciences (neurology and neurosurgery) to meet future demand for such therapies. STATE OF THE ART: Preclinical vector-based gene therapy approaches have been translated into clinical trials for PD and other neurodegenerative conditions. Unfortunately, such trials, and parallel efforts using other therapeutics, have yet to provide a breakthrough. Image-guided convection enhanced delivery (CED) optimises the parenchymal distribution of gene therapies applied within the CNS, and may ultimately provide such a breakthrough. CLINICAL IMPLICATIONS: Currently, image-guided CED and gene therapy are not part of training programmes for most neurosurgeons and neurologists. As a result, few medical centres and hospitals have sufficiently experienced teams to participate in gene transfer clinical trials for PD or other neurological conditions. If CNS gene therapies prove to be efficacious for PD and/or other conditions, the demand for such treatments will overwhelm the available number of experienced clinical neuroscience teams and treatment centres. FUTURE DIRECTIONS: Expanded indications and demand for CNS gene therapies will require a worldwide educational effort to supplement the training of clinical neuroscience practitioners. Initially, a limited number of Centres of Excellence will need to establish relevant educational training requirements and best practice for such therapeutic approaches. Advanced technologies, including robotics and artificial intelligence, are especially germane in this regard, and will expand the treatment team's capabilities while assisting in the safe and timely care of those afflicted.

Bridging the Great Divide: What Can Neurology Learn From Psychiatry?

Neurology and psychiatry share common historical origins and rely on similar tools to study brain disorders. Yet the practical integration of medical and scientific approaches across these clinical neurosciences remains elusive. Although much has been written about the need to incorporate emerging systems-level, cellular-molecular, and genetic-epigenetic advances into a science of mind for psychiatric disorders, less attention has been given to applying clinical neuroscience principles to conceptualize neurologic conditions with an integrated neurobio-psycho-social approach. In this perspective article, the authors briefly outline the historically interwoven and complicated relationship between neurology and psychiatry. Through a series of vignettes, the authors then illustrate how some traditional psychiatric conditions are being reconceptualized in part as disorders of neurodevelopment and awareness. They emphasize the intersection of neurology and psychiatry by highlighting conditions that cut across traditional diagnostic boundaries. The authors argue that the divide between neurology and psychiatry can be narrowed by moving from lesion-based toward circuit-based understandings of neuropsychiatric disorders, from unidirectional toward bidirectional models of brain-behavior relationships, from exclusive reliance on categorical diagnoses toward transdiagnostic dimensional perspectives, and from silo-based research and treatments toward interdisciplinary approaches. The time is ripe for neurologists and psychiatrists to implement an integrated clinical neuroscience approach to the assessment and management of brain disorders. The subspecialty of behavioral neurology & neuropsychiatry is poised to lead the next generation of clinicians to merge brain science with psychological and social-cultural factors. These efforts will catalyze translational research, revitalize training programs, and advance the development of impactful patient-centered treatments.

The Establishment of an On-Campus Neurotraining Center at a Small University: Internship Experience Using Neuropsychological Techniques.

In 2015, Wilkes University's Neuroscience Program and Psychology Department established a unique training and learning center on a small liberal arts undergraduate campus - The NeuroTraining & Research Center. This paper shares the purpose of the Center, as a learning tool for Neuroscience majors who engage in internships and research opportunities, and as a means of promoting well-being on the campus by offering training in techniques such as Neurofeedback, Biofeedback and Audio-Visual Entrainment to the college community. The role that the center plays in connecting real world applications to concepts in Neuroscience, and the approach that the authors have taken to assess student learning is presented in this article.

Quickly diagnosing Bietti crystalline dystrophy with deep learning.

Bietti crystalline dystrophy (BCD) is an autosomal recessive inherited retinal disease (IRD) and its early precise diagnosis is much challenging. This study aims to diagnose BCD and classify the clinical stage based on ultra-wide-field (UWF) color fundus photographs (CFPs) via deep learning (DL). All CFPs were labeled as BCD, retinitis pigmentosa (RP) or normal, and the BCD patients were further divided into three stages. DL models ResNeXt, Wide ResNet, and ResNeSt were developed, and model performance was evaluated using accuracy and confusion matrix. Then the diagnostic interpretability was verified by the heatmaps. The models achieved good classification results. Our study established the largest BCD database of Chinese population. We developed a quick diagnosing method for BCD and evaluated the potential efficacy of an automatic diagnosis and grading DL algorithm based on UWF fundus photography in a Chinese cohort of BCD patients.

Exploring sexual dimorphism in basal forebrain volume changes during aging and neurodegenerative diseases.

Patients with neurodegenerative diseases exhibit diminished basal forebrain (BF) volume compared to healthy individuals. However, it's uncertain whether this difference is consistent between sexes. It has been reported that BF volume moderately atrophies during aging, but the effect of sex on BF volume changes during the normal aging process remains unclear. In the cross-sectional study, we observed a significant reduction in BF volume in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared to Healthy Controls (HCs), especially in the Ch4 subregion. Notably, significant differences in BF volume between MCI and HCs were observed solely in the female group. Additionally, we identified asymmetrical atrophy in the left and right Ch4 subregions in female patients with AD. In the longitudinal analysis, we found that aging seemed to have a minimal impact on BF volume in males. Our study highlights the importance of considering sex as a research variable in brain science.

IL-6 is associated with poor seizure control in low-grade glioma patients undergoing primary resection.

In this study, 198 patients with low-grade gliomas (LGGs) undergoing primary resection were evaluated for seizure status at 24 months after primary resection with the Engel classification of seizures, and 120 patients had good seizure control (class I) while 78 patients had poor seizure control (class II-IV). Multivariate analysis showed that cortex involvement, subtotal resection, serum IL-6 concentration, and neutrophil to lymphocyte ratio (NLR) were associated with poor seizure control. The area under curve (AUC) of serum IL-6 concentration, NLR and their combination applied in predicting poor seizure control was 0.756, 0.714, and 0.857, respectively. The AUC of combination prediction was significantly higher than those of individual prediction. Therefore, elevated serum IL-6 concentration was associated with poor seizure control in LLG patients undergoing primary resection and could be applied in predicting seizure control, and the predictive value could be elevated through adding other serum indices to IL-6.

Spatial navigation questionnaires as a supportive diagnostic tool in early Alzheimer's disease.

Impaired spatial navigation is early marker of Alzheimer's disease (AD). We examined ability of self- and informant-reported navigation questionnaires to discriminate between clinically and biomarker-defined participants, and associations of questionnaires with navigation performance, regional brain atrophy, AD biomarkers, and biomarker status. 262 participants (cognitively normal, with subjective cognitive decline, amnestic mild cognitive impairment [aMCI], and mild dementia) and their informants completed three navigation questionnaires. Navigation performance, magnetic resonance imaging volume/thickness of AD-related brain regions, and AD biomarkers were measured. Informant-reported questionnaires distinguished between cognitively normal and impaired participants, and amyloid-ß positive and negative aMCI. Lower scores were associated with worse navigation performance, greater atrophy in AD-related brain regions, and amyloid-ß status. Self-reported questionnaire scores did not distinguish between the groups and were weakly associated with navigation performance. Other associations were not significant. Informant-reported navigation questionnaires may be a screening tool for early AD reflecting atrophy of AD-related brain regions and AD pathology.

Training in neuropsychiatry: views of early career psychiatrists from across the world.

BACKGROUND: Training and practice in neuropsychiatry varies across the world. However, little is known about the experiences and opinions of early career psychiatrists (ECPs) across different countries regarding neuropsychiatry. AIMS AND METHOD: To investigate neuropsychiatry training experiences, practices and opinions among ECPs across different countries. An online survey was distributed to ECPs in 35 countries across the world. RESULTS: A total of 522 participants took part in this study. Responses show that neuropsychiatry is integrated to a variable extent in psychiatric training curricula across the world. Most respondents were not aware of the existence of neuropsychiatric training or of neuropsychiatric units. Most agreed that training in neuropsychiatry should be done during or after the psychiatry training period. Lack of interest among specialty societies, lack of time during training, and political and economic reasons are regarded as the main barriers. CLINICAL IMPLICATIONS: These findings call for an improvement in the extent and in the quality of neuropsychiatry training across the world.