Postictal psychiatric symptoms: A neurophysiological study.

OBJECTIVE: Postictal psychiatric symptoms (PPS) are a relatively common but understudied phenomenon in epilepsy. The mechanisms by which seizures contribute to worsening in psychiatric symptoms are unclear. We aimed to identify PPS prospectively during and after admission to the epilepsy monitoring unit (EMU) in order to characterize the postictal physiologic changes leading to PPS. METHODS: We prospectively enrolled patients admitted to the EMU and administered repeat psychometric questionnaires during and after their hospital stay in order to assess for postictal exacerbations in four symptom complexes: anger/hostility, anxiety, depression, and paranoia. Electroclinical and electrographic seizures were identified from the EEG recordings, and seizure durations were measured. The severity of postictal slowing was calculated as the proportion of postictal theta/delta activity in the postictal EEG relative to the preictal EEG using the Hilbert transform. RESULTS: Among 33 participants, 8 demonstrated significant increases in at least one of the four symptoms (the PPS+ group) within three days following the first seizure. The most common PPS was anger/hostility, experienced by 7/8 participants with PPS. Among the 8 PPS+ participants, four experienced more than one PPS. As compared to those without PPS (the PPS- group), the PPS+ group demonstrated a greater degree of postictal EEG slowing at 10 min (p = 0.022) and 20 min (p = 0.05) following seizure termination. They also experienced significantly more seizures during the study period (p = 0.005). There was no difference in seizure duration between groups. SIGNIFICANCE: Postictal psychiatric symptoms including anger/hostility, anxiety, depression, and paranoia may be more common than recognized. In particular, postictal increases in anger and irritability may be particularly common. We provide physiological evidence of a biological mechanism as well as a demonstration of the use of quantitative electroencephalography toward a better understanding of postictal neurophysiology.

Glial Activity Load on PET Reveals Persistent "Smoldering" Inflammation in MS Despite Disease-Modifying Treatment: 18F-PBR06 Study.

PURPOSE OF THE REPORT: 18F-PBR06-PET targeting 18-kDa translocator protein can detect abnormal microglial activation (MA) in multiple sclerosis (MS). The objectives of this study are to develop individualized mapping of MA using 18F-PBR06, to determine the effect of disease-modifying treatment (DMT) efficacy on reducing MA, and to determine its clinical, radiological, and serological correlates in MS patients. PATIENTS AND METHODS: Thirty 18F-PBR06-PET scans were performed in 22 MS patients (mean age, 46 ± 13 years; 16 females) and 8 healthy controls (HCs). Logarithmically transformed "glial activity load on PET" scores (calculated as the sum of voxel-by-voxel z-scores ≥4), "lnGALP," were compared between MS and HC and between MS subjects on high-efficacy DMTs (H-DMT, n = 13) and those on no or lower-efficacy treatment, and correlated with clinical measures, serum biomarkers, and cortical thickness. RESULTS: Cortical gray matter (CoGM) and white matter (WM) lnGALP scores were higher in MS versus HC (+33% and +48%, P < 0.001). In H-DMT group, CoGM and WM lnGALP scores were significantly lower than lower-efficacy treatment (P < 0.01) but remained abnormally higher than in HC group (P = 0.006). Within H-DMT patients, CoGM lnGALP scores correlated positively with physical disability, fatigue and serum glial fibrillary acid protein levels (r = 0.65-0.79, all P's < 0.05), and inversely with cortical thickness (r = -0.66, P < 0.05). CONCLUSIONS: High-efficacy DMTs decrease, but do not normalize, CoGM and WM MA in MS patients. Such "residual" MA in CoGM is associated with clinical disability, serum biomarkers, and cortical degeneration. Individualized mapping of translocator protein PET using 18F-PBR06 is clinically feasible and can potentially serve as an imaging biomarker for evaluating "smoldering" inflammation in MS patients.

The Pain Intervention & Digital Research Program: an operational report on combining digital research with outpatient chronic disease management.

Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year. Chronic pain is an instantiation of the perennial complexity of how to best assess and treat chronic diseases over time, especially in populations where age, medical comorbidities, and socioeconomic barriers may limit access to care. Chronic disease management poses a particular challenge for the healthcare system's transition from fee-for-service to value and risk-based reimbursement models. Remote, passive real-time data from smartphones could enable more timely interventions and simultaneously manage risk and promote better patient outcomes through predicting and preventing costly adverse outcomes; however, there is limited evidence whether remote monitoring is feasible, especially in the case of older patients with chronic pain. Here, we introduce the Pain Intervention and Digital Research (Pain-IDR) Program as a pilot initiative launched in 2022 that combines outpatient clinical care and digital health research. The Pain-IDR seeks to test whether functional status can be assessed passively, through a smartphone application, in older patients with chronic pain. We discuss two perspectives-a narrative approach that describes the clinical settings and rationale behind changes to the operational design, and a quantitative approach that measures patient recruitment, patient experience, and HERMES data characteristics. Since launch, we have had 77 participants with a mean age of 55.52, of which n = 38 have fully completed the 6 months of data collection necessitated to be considered in the study, with an active data collection rate of 51% and passive data rate of 78%. We further present preliminary operational strategies that we have adopted as we have learned to adapt the Pain-IDR to a productive clinical service. Overall, the Pain-IDR has successfully engaged older patients with chronic pain and presents useful insights for others seeking to implement digital phenotyping in other chronic disease settings.

Toward a Unified Classification System for Brain-Mind Disorders: Putting Calls for Integrated Clinical Neuroscience Into Action.

Dividing the brain-mind into the specialized fields of neurology and psychiatry has produced many granular advantages, but these silos have imposed barriers to comprehensively understanding and contextualizing the fundamentals governing mental life and its maladies. Scientific inquiry into these fundamentals cannot reach its full potential without interdigitating the boundaries of two specialties of the same organ for both scholarship and clinical practice. We propose that to truly integrate disorders of the brain and the mind for research and clinical care, we must carefully reexamine the classification of its disorders (nosology) as an instrument to develop a coherent pathological and psychological framework. We call on professional organizations from neurology, psychiatry, behavioral neurology, neuropsychiatry, neuropsychology, and other relevant subspecialties (eg, geriatric psychiatry) to convene a multidisciplinary task force to define the current classification principles of their subspecialties and work toward developing an integrated nosology. The effect of a shared classification system, which we acknowledge is a difficult proposition philosophically and politically, would have transformative potential across educational, clinical, scientific, programmatic, and sociocultural realms. If accomplished, this initiative would provide a definitive step toward reducing stigma (and promoting reimbursement parity) for the full spectrum of complex brain disorders (regardless of traditional neurologic vs psychiatric conceptualizations).

Neuroethical implications of focused ultrasound for neuropsychiatric illness.

BACKGROUND: MR-guided focused ultrasound is a promising intervention for treatment-resistant mental illness, and merits contextualized ethical exploration in relation to more extensive ethical literature regarding other psychosurgical and neuromodulation treatment options for this patient population. To our knowledge, this topic has not yet been explored in the published literature. OBJECTIVE: The purpose of this paper is to review and discuss in detail the neuroethical implications of MR-guided focused ultrasound for neuropsychiatric illness as an emerging treatment modality. METHODS: Due to the lack of published literature on the topic, the approach involved a detailed survey and review of technical and medical literature relevant to focused ultrasound and established ethical issues related to alternative treatment options for patients with treatment-resistant, severe and persistent mental illness. The manuscript is structured according to thematic and topical findings. RESULTS: This technology has potential benefits for patients suffering with severe mental illness, compared with established alternatives. The balance of technical, neuroscientific and clinical considerations should inform ethical deliberations. The nascent literature base, nuances in legal classification and permissibility depending upon jurisdiction, influences of past ethical issues associated with alternative treatments, tone and framing in media articles, and complexity of clinical trials all influence ethical assessment and evaluations of multiple stakeholders. Recommendations for future research are provided based on these factors. CONCLUSION: Salient ethical inquiry should be further explored by researchers, clinicians, and ethicists in a nuanced manner methodologically, one which is informed by past and present ethical issues related to alternative treatment options, broader psychiatric treatment frameworks, pragmatic implementation challenges, intercultural considerations, and patients' ethical concerns.

The Amplification of Symptoms in the Medically Ill.

The mechanism of symptom amplification, developed in the study of somatization, may be helpful in caring for patients with symptoms that, while they have a demonstrable medical basis, are nonetheless disproportionately severe and distressing. Amplified medical symptoms are marked by disproportionate physical suffering, unduly negative thoughts and concerns about them, and elevated levels of health-related anxiety. They are accompanied by extensive and sustained illness behaviors, disproportionate difficulty compartmentalizing them and circumscribing their impact, and consequent problems and dissatisfaction with their medical care. A distinction has long been made between "medically explained" and "medically unexplained" symptoms. However, a more comprehensive view of symptom phenomenology undermines this distinction and places all symptoms along a smooth continuum regardless of cause: Recent findings in cognitive neuroscience suggest that all symptoms-regardless of origin-are processed through convergent pathways. The complete conscious experience of both medically "explained" and "unexplained" symptoms is an amalgam of a viscerosomatic sensation fused with its ascribed salience and the patient's ideas, expectations, and concerns about the sensation. This emerging empirical evidence furnishes a basis for viewing persistent, disproportionately distressing symptoms of demonstrable disease along a continuum with medically unexplained symptoms. Thus, therapeutic modalities developed for somatization and medically unexplained symptoms can be helpful in the care of seriously ill medical patients with amplified symptoms. These interventions include educational groups for coping with chronic illness, cognitive therapies for dysfunctional thoughts, behavioral strategies for maladaptive illness behaviors, psychotherapy for associated emotional distress, and consultation with mental health professionals to assist the primary care physician with difficulties in medical management.

Cortical and Subcortical Dysmetabolism Are Dynamic Markers of Clinical Disability and Course in Anti-LGI1 Encephalitis.

BACKGROUND AND OBJECTIVES: This [18F]fluorodeoxyglucose (FDG) PET study evaluates the accuracy of semiquantitative measurement of putaminal hypermetabolism in identifying anti-leucine-rich, glioma-inactivated-1 (LGI1) protein autoimmune encephalitis (AE). In addition, the extent of brain dysmetabolism, their association with clinical outcomes, and longitudinal metabolic changes after immunotherapy in LGI1-AE are examined. METHODS: FDG-PET scans from 49 age-matched and sex-matched subjects (13 in LGI1-AE group, 15 in non-LGI1-AE group, 11 with Alzheimer disease [AD], and 10 negative controls [NCs]) and follow-up scans from 8 patients with LGI1 AE on a median 6 months after immunotherapy were analyzed. Putaminal standardized uptake value ratios (SUVRs) normalized to global brain (P-SUVRg), thalamus (P/Th), and midbrain (P/Mi) were evaluated for diagnostic accuracy. SUVRg was applied for all other analyses. RESULTS: P-SUVRg, P/Th, and P/Mi were higher in LGI1-AE group than in non-LGI1-AE group, AD group, and NCs (all p < 0.05). P/Mi and P-SUVRg differentiated LGI1-AE group robustly from other groups (areas under the curve 0.84-0.99). Mediotemporal lobe (MTL) SUVRg was increased in both LGI1-AE and non-LGI1-AE groups when compared with NCs (both p < 0.05). SUVRg was decreased in several frontoparietal regions and increased in pallidum, caudate, pons, olfactory, and inferior occipital gyrus in LGI1-AE group when compared with that in NCs (all p < 0.05). In LGI1-AE group, both MTL and putaminal hypermetabolism were reduced after immunotherapy. Normalization of regional cortical dysmetabolism associated with clinical improvement at the 6- and 20-month follow-up. DISCUSSION: Semiquantitative measurement of putaminal hypermetabolism with FDG-PET may be used to distinguish LGI1-AE from other pathologies. Metabolic abnormalities in LGI1-AE extend beyond putamen and MTL into other subcortical and cortical regions. FDG-PET may be used in evaluating disease evolution in LGI1-AE. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that semiquantitative measures of putaminal metabolism on PET can differentiate patients with LGI1-AE from patients without LGI1-AE, patients with AD, or NCs.

Localized task-invariant emotional valence encoding revealed by intracranial recordings.

The ability to distinguish between negative, positive and neutral valence is a key part of emotion perception. Emotional valence has conceptual meaning that supersedes any particular type of stimulus, although it is typically captured experimentally in association with particular tasks. We sought to identify neural encoding for task-invariant emotional valence. We evaluated whether high-gamma responses (HGRs) to visually displayed words conveying emotions could be used to decode emotional valence from HGRs to facial expressions. Intracranial electroencephalography was recorded from 14 individuals while they participated in two tasks, one involving reading words with positive, negative, and neutral valence, and the other involving viewing faces with positive, negative, and neutral facial expressions. Quadratic discriminant analysis was used to identify information in the HGR that differentiates the three emotion conditions. A classifier was trained on the emotional valence labels from one task and was cross-validated on data from the same task (within-task classifier) as well as the other task (between-task classifier). Emotional valence could be decoded in the left medial orbitofrontal cortex and middle temporal gyrus, both using within-task classifiers and between-task classifiers. These observations suggest the presence of task-independent emotional valence information in the signals from these regions.

Diversity, Equity, and Inclusion Committee: An Instrument to Champion Diversity Efforts Within a Large Academic Psychiatry Department.

Diversity, equity, and inclusion (DEI) have become increasingly recognized as essential to the practice of high-quality patient care delivery and the support of members of the clinical environment. A solid understanding of DEI contributes to a better grasp of what drives health care disparities and yields improved clinical outcomes for minority populations. This column discusses how individuals can practically promote DEI by describing the design and implementation of DEI in an academic psychiatry department. The authors highlight the powerful role of departmental initiatives in establishing best practices for DEI and lessons learned through the work of the psychiatry department's DEI committee.

Decision Models and Technology Can Help Psychiatry Develop Biomarkers.

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.