Treatment Response In Individual Patients Research Articles

Bevacizumab-Induced Hypertension in Glioblastoma Patients and Its Potential as a Modulator of Treatment Response.

Glioblastoma invasion is the primary mechanism responsible for its dismal prognosis and is the direct result of interactions between glioblastoma cells and the tumor vasculature. The dysregulated microvasculature in glioblastoma tumors and vessels co-opted from surrounding brain tissue support rapid tumor growth and are utilized as pathways for invasive cancer cells. Attempts to target the glioblastoma vasculature with antiangiogenic agents (eg, bevacizumab) have nonetheless shown limited and inconsistent efficacy, and the underlying causes of such heterogeneous responses remain unknown. Several studies have identified that patients with glioblastoma who develop hypertension following treatment with bevacizumab show significant improvement in overall survival compared with normotensive nonresponders. Here we review these findings and discuss the potential of hypertension as a biomarker for glioblastoma treatment response in individual patients and the role of hypertension as a modulator of interactions between tumor cells and cells in the perivascular niche. We suggest that a better understanding of the actions of bevacizumab and hypertension at the cellular level will contribute to developing more effective personalized therapies that address glioblastoma tumor cell invasion.

Predicting treatment response in adolescents and young adults with major depressive episodes from fMRI using graph isomorphism network

BackgroundMajor depressive episode (MDE) is the main clinical feature of mood disorders (major depressive disorder and bipolar disorder) in adolescents and young adults and accounts for most of the disease course. However, 30%-40% of MDE patients not responding to clinical first-line interventions. It is crucial to predict treatment response in the early stages and identify biomarkers associated with treatment response. Graph Isomorphism Network (GIN), a deep learning method, is promising for predicting treatment response for individual MDE patients with more powerful representation ability to capture the features of brain functional connectivity. MethodsIn this study, GIN was used to predict individual treatment response in 198 adolescents and young adults with MDE. The most discriminating regions were also identified for the treatment response prediction. ResultsUsing GIN approach, the baseline functional connectivity could predict 79.8% responders and 67.4% non-responders to treatment (accuracy 74.24%). Furthermore, the most discriminating brain regions were mainly involved in paralimbic and subcortical areas. ConclusionsGIN has shown potential in predicting treatment response for individual patients, which may enable personalized treatment decisions. Furthermore, targeted interventions focused on modulating the activity and connectivity within paralimbic and subcortical regions could potentially improve treatment outcomes and enable personalized interventions for adolescents and young adults with MDE.

Plasma levels of interleukin‐8 and response to paroxetine in patients with major depressive disorder

Immune dysregulation plays a key role in major depressive disorder (MDD). However, little is known about the complicated involvement of various interleukins in MDD. This study was performed to investigate the correlation between plasma interleukin-8 (IL-8) levels and treatment outcome of paroxetine (a selective serotonin reuptake inhibitor) in patients with MDD. A total of 115 hospitalized patients (36 males and 79 females), aged from 18 to 72years, were enrolled. Plasma levels of IL-8 were measured before treatment initiation (baseline) and at 8weeks after oral paroxetine treatment. Efficacy of paroxetine was evaluated by use of the Hamilton Depression Rating Scale (HAMD-17). Baseline IL-8 levels were compared between responders and non-responders to paroxetine treatment. Plasma IL-8 levels decreased significantly after an 8-week antidepressant treatment in responders, in association with a dramatic decrease in HAMD-17 scores. In non-responders, plasma IL-8 levels did not change significantly at 8weeks after antidepressant treatment. Baseline plasma IL-8 levels were found to be significantly lower in responders than in non-responders, showing a correlation between IL-8 and antidepressant response to paroxetine. These results indicate that plasma IL-8 levels were related to treatment outcome of paroxetine, and therefore suggest that IL-8 could be a promising predicator of treatment response in individual patients with MDD.

Abstract 1159: Predicting clinical drug responses using a few-shot learning-based interpretable AI

Abstract High-throughput screens have generated large amounts of data characterizing how thousands of cell lines respond to hundreds of anti-cancer therapies. However, predictive drug response models trained using data from cell lines often fail to translate to clinical applications. Here, we focus on two key issues to improve clinical performance: 1) Transferability: the ability of predictive models to quickly adapt to clinical contexts even with a limited number of samples from patients, and 2) Interpretability: the ability to explain how drug-response predictions are being made given an individual patient’s genotype. Notably, an interpretable AI model can also help to identify biomarkers of treatment response in individual patients. By leveraging new developments in meta-learning and interpretable AI, we have developed an interpretable drug response prediction model that is trained on large amounts of data from experiments using cell lines and then transferred to clinical applications. We assessed our model’s clinical utility using AACR Project GENIE data, which contains mutational profiles from tumors and the patient’s therapeutic responses. We have demonstrated the feasibility of applying our AI-driven predictive model to clinical settings and shown how this model can support clinical decision making for tumor boards. Citation Format: Sungjoon Park, Akshat Singhal, Erica Silva, Jason F. Kreisberg, Trey Ideker. Predicting clinical drug responses using a few-shot learning-based interpretable AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1159.

Acoustic Bioprinting of Patient-Derived Organoids for Predicting Cancer Therapy Responses.

Cancer models, which are biologically representative of patient tumors, can predict the treatment responses and help determine the most appropriate cancer treatment for individual patients. Here, a point-of-care testing system called acoustically bioprinted patient-derived microtissues (PDMs) that can model cancer invasion and predict treatment response in individual patients with colorectal cancer (CRC), is reported. The PDMs are composed of patient-derived colorectal tumors and healthy organoids which can be precisely arranged by acoustic bioprinting approach for recapulating primary tissue's architecture. Particularly, these tumor organoids can be efficiently generated and can apprehend histological, genomic, and phenotypical characteristics of primary tumors. Consequently, these PDMs allow physiologically relevant in vitro drug (5-fluorouracil) screens, thus predicting the paired patient's responses to chemotherapy. A correlation between organoid invasion speed and normalized spreading speed of the paired patients is further established. It provides a quantitative indicator to help doctors make better decisions on ultimate anus-preserving operation for extremely low CRC patients. Thus, by combing acoustic bioprinting and organoid cultures, this method may open an avenue to establish complex 3D tissue models for precision and personalized medicine.

Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity

Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD). However, response varies considerably among individuals. Currently, no biomarkers are available to assist clinicians in identifying youth who are most likely to benefit from treatment. In this study, we investigated whether resting-state functional magnetic resonance imaging (rs-fMRI) could distinguish between responders and non-responders on the group- and individual patient level. Pre-treatment rs-fMRI was recorded in 40 youth (ages 8–17 years) with (partial) PTSD before trauma-focused psychotherapy. Change in symptom severity from pre- to post-treatment was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders (≥30% symptom reduction) and non-responders. Functional networks were identified using meta-independent component analysis. Group-differences within- and between-network connectivity between responders and non-responders were tested using permutation testing. Individual predictions were made using multivariate, cross-validated support vector machine classification. A network centered on the bilateral superior temporal gyrus predicted treatment response for individual patients with 76% accuracy (pFWE = 0.02, 87% sensitivity, 65% specificity, area-under-receiver-operator-curve of 0.82). Functional connectivity between the frontoparietal and sensorimotor network was significantly stronger in non-responders (t = 5.35, pFWE = 0.01) on the group-level. Within-network connectivity was not significantly different between groups. This study provides proof-of-concept evidence for the feasibility to predict trauma-focused psychotherapy response in youth with PTSD at an individual-level. Future studies are required to test if larger cohorts could increase accuracy and to test further generalizability of the prediction models.

Avatar acceptability: views from the Australian Cystic Fibrosis community on the use of personalised organoid technology to guide treatment decisions.

BackgroundPatient-oriented research approaches that reflect the needs and priorities of those most affected by health research outcomes improves translation of research findings into practice. Targeted therapies for cystic fibrosis (CF) are now a viable treatment option for some eligible individuals despite the heterogeneous patient-specific therapeutic response. This has necessitated development of a clinical tool that predicts treatment response for individual patients. Patient-derived mini-organs (organoids) have been at the forefront of this development. However, little is known about their acceptability in CF patients and members of the public.MethodsWe used a cross-sectional observational design to conduct an online survey in people with CF, their carers and community comparisons. Acceptability was examined in five domains: 1) willingness to use organoids, 2) perceived advantages and disadvantages of organoids, 3) acceptable out-of-pocket costs, 4) turnaround time and 5) source of tissue.ResultsIn total, 188 participants completed the questionnaire, including adults with CF and parents of children with CF (90 (48%)), and adults without CF and parents of children without CF (98 (52%)). Use of organoids to guide treatment decisions in CF was acceptable to 86 (95%) CF participants and 98 (100%) community participants. The most important advantage was that organoids may improve treatment selection, improving the patient's quality of life and life expectancy. The most important disadvantage was that the organoid recommended treatment might be unavailable or too expensive.ConclusionsThese findings indicate acceptance of patient-derived organoids as a tool to predict treatment response by the majority of people surveyed. This may indicate successful future implementation into healthcare systems.

Human Colon Organoids and Other Laboratory Strategies to Enhance Patient Treatment Selection.

Though many advancements in personalized medicine have been made, better methods are still needed to predict treatment benefit for patients with colorectal cancer. Patient-derived cancer organoids (PDCOs) are a major advance towards true personalization of treatment strategies. A growing body of literature is demonstrating the feasibility of PDCOs as an accurate and high-throughput preclinical tool for patient treatment selection. Many studies demonstrate that these cultures are readily generated and represent the tumors they were derived from phenotypically and based on their mutation profile. This includes maintenance of the driver muatations giving the cancer cells a selective growth advantage, and also heterogeneity, including molecular and metabolic heterogeneity. Additionally, PDCOs are now being utilized to develop patient biospecimen repositories, perform high to moderate-throughput drug screening, and to potentially predict treatment response for individual patients that are undergoing anti-cancer treatments. In order to develop PDCOs as a true clinical tool, further studies are required to determine the reproducibility and accuracy of these models to predict patient response.

TMOD-15. IDENTIFYING THE SPATIAL AND TEMPORAL DYNAMICS OF GLIOBLASTOMA SUBPOPULATIONS WITHIN INDIVIDUAL PATIENTS

Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.

Development and initial validation of the Asthma Severity Scoring System (ASSESS)

Tools for quantification of asthma severity are limited. We sought to develop a continuous measure of asthma severity, the Asthma Severity Scoring System (ASSESS), for adolescents and adults, incorporating domains of asthma control, lung function, medications, and exacerbations. Baseline and 36-month longitudinal data from participants in phase 3 of the Severe Asthma Research Program (NCT01606826) were used. Scale properties, responsiveness, and a minimally important difference were determined. External replication was performed in participants enrolled in the Severe Asthma Research Program phase 1/2. The utility of ASSESS for detecting treatment response was explored in participants undergoing corticosteroid responsiveness testing with intramuscular triamcinolone and participants receiving biologics. ASSESS scores ranged from 0 to 20 (8.78± 3.9; greater scores reflect worse severity) and differed among 5 phenotypic groups. Measurement properties were acceptable. ASSESS was responsive to changes in quality of life with a minimally important difference of 2, with good specificity for outcomes of asthma improvement and worsening but poor sensitivity. Replication analyses yielded similar results, with a 2-point decrease (improvement) associated with improvements in quality of life. Participants with a 2-point or greater decrease (improvement) in ASSESS scores also had greater improvement in lung function and asthma control after triamcinolone, but these differences were limited to phenotypic clusters 3, 4, and 5. Participants treated with biologics also had a 2-point or greater decrease (improvement) in ASSESS scores overall. The ASSESS tool is an objective measure that might be useful in epidemiologic and clinical research studies for quantification of treatment response in individual patients and phenotypic groups. However, validation studies are warranted.

Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses

Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results.Materials and methods: We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses.Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index = 0.89).Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.

S27. Predicting Trauma-Focused Therapy Outcome From Resting-State Functional Magnetic Resonance Imaging in Veterans With Posttraumatic Stress Disorder

Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. Here, we tested whether resting-state functional magnetic imaging (rs-fMRI) can predict treatment response for individual patients.

Optoacoustic Detection of Early Therapy-Induced Tumor Cell Death Using a Targeted Imaging Agent.

Purpose: The development of new treatments and their deployment in the clinic may be assisted by imaging methods that allow an early assessment of treatment response in individual patients. The C2A domain of Synaptotagmin-I (C2Am), which binds to the phosphatidylserine (PS) exposed by apoptotic and necrotic cells, has been developed as an imaging probe for detecting cell death. Multispectral optoacoustic tomography (MSOT) is a real-time and clinically applicable imaging modality that was used here with a near infrared (NIR) fluorophore-labeled C2Am to image tumor cell death in mice treated with a TNF-related apoptosis-inducing ligand receptor 2 (TRAILR2) agonist and with 5-fluorouracil (5-FU).Experimental Design: C2Am was labeled with a NIR fluorophore and injected intravenously into mice bearing human colorectal TRAIL-sensitive Colo205 and TRAIL-resistant HT-29 xenografts that had been treated with a potent agonist of TRAILR2 and in Colo205 tumors treated with 5-FU.Results: Three-dimensional (3D) MSOT images of probe distribution showed development of tumor contrast within 3 hours of probe administration and a signal-to-background ratio in regions containing dead cells of >10 after 24 hours. A site-directed mutant of C2Am that is inactive in PS binding showed negligible binding. Tumor retention of the active probe was strongly correlated (R2 = 0.97, P value < 0.01) with a marker of apoptotic cell death measured in histologic sections obtained post mortem.Conclusions: The rapid development of relatively high levels of contrast suggests that NIR fluorophore-labeled C2Am could be a useful optoacoustic imaging probe for detecting early therapy-induced tumor cell death in the clinic. Clin Cancer Res; 23(22); 6893-903. ©2017 AACR.

Biomarkers for evaluation of treatment response in classical Hodgkin lymphoma: comparison of sGalectin-1, sCD163 and sCD30 with TARC.

Soluble Galectin-1 (sGal-1, also termed LGALS1), soluble CD163 (sCD163) and soluble CD30 (sCD30) have been reported to be elevated in plasma or serum of patients with classical Hodgkin lymphoma (cHL). We aimed to determine the clinical utility of these biomarkers for evaluation of treatment response compared to thymus and activation regulated chemokine (TARC, also termed CCL17). Plasma or serum samples were prospectively collected among 103 newly diagnosed cHL patients before and after treatment. Levels of sGal-1, sCD163, sCD30 and TARC were correlated with disease characteristics and clinical treatment response. Elevated plasma levels of sGal-1, sCD163, sCD30 and TARC were found in 67%, 21%, 91% and 93% of cHL patients respectively. Mean plasma levels of sGal-1 and sCD30 decreased after treatment but sCD163 did not decrease after treatment. There was no correlation with change of these markers and clinical treatment response in individual patients. TARC levels strongly correlated with disease characteristics and metabolic volume. TARC remained high in 6 out of 7 non-responsive patients and dramatically decreased in 95 out of 96 responsive patients. In summary, elevated pre-treatment levels of sGal-1, sCD163, sCD30 and TARC can be found in patients with cHL. However, only plasma TARC accurately reflects disease activity and correlates with clinical treatment response.

Clearance of circulating leukemic blasts during induction therapy to predict treatment response regardless of cytogenetic risk or age in adult acute myeloid leukemia.

e18522Background: Despite the development of prognostic factors for stratification models in AML patients, treatment response in individual patient has not been predicted accurately. Early assessme...

Molecular classification of idiopathic pulmonary fibrosis: personalized medicine, genetics and biomarkers.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrotic lung disease associated with high morbidity and poor survival. Characterized by substantial disease heterogeneity, the diagnostic considerations, clinical course and treatment response in individual patients can be variable. In the past decade, with the advent of high-throughput proteomic and genomic technologies, our understanding of the pathogenesis of IPF has greatly improved and has led to the recognition of novel treatment targets and numerous putative biomarkers. Molecular biomarkers with mechanistic plausibility are highly desired in IPF, where they have the potential to accelerate drug development, facilitate early detection in susceptible individuals, improve prognostic accuracy and inform treatment recommendations. Although the search for candidate biomarkers remains in its infancy, attractive targets such as MUC5B and MPP7 have already been validated in large cohorts and have demonstrated their potential to improve clinical predictors beyond that of routine clinical practices. The discovery and implementation of future biomarkers will face many challenges, but with strong collaborative efforts among scientists, clinicians and the industry the ultimate goal of personalized medicine may be realized.

Assessment of Hematologic and Visceral Parameters in Patients with Type 1 Gaucher Disease in Spain: An Epidemiological Study Using the Therapeutic Goals MAP (Monitor, Action and Progress) Tool®

BACKGROUNDType 1 Gaucher disease (GD1) is caused by a hereditary deficiency of glucocerebrosidase that results in accumulation of glucosylceramide primarily in the lysosomes of macrophages. GD1 patients commonly present with anemia, thrombocytopenia and hepatosplenomegaly. Due to the clinical heterogeneity of GD1, a set of therapeutic goals was published to assist in individualized disease management. The MAP Tool® was designed based on these therapeutic goals to facilitate monitoring of GD1 progression and treatment response in individual patients in a standardized way. OBJECTIVEAn observational epidemiological survey was conducted to describe the clinical characteristics and health status of GD1 patients managed in specialized centers in Spain using the Therapeutic Goals MAP Tool®. METHODSThis study was conducted in hospitals geographically representing 10 of Spain’s autonomous regions. GD1 patients attending a routine clinical visit during the specified recruitment period (June 2012 to January 2013) were invited to participate. Individual patient data, including information on GD1 history and treatment, were collected retrospectively from clinical records and documented using the MAP Tool®. Therapeutic goals for seven parameters were chosen as primary outcomes measures, including goals for hemoglobin concentration, platelet count, liver volume and spleen volume. We also examined the achievement of therapeutic goals in patients grouped according to treatment status, splenectomy status, and gender. RESULTSA total of 108 GD1 patients were recruited from 28 hospitals over the 7-month study recruitment period. Patients had a mean age of 45 years, 9 (8%) were pediatric patients (≤18 years of age), and 57 (53%) were male. Ninety-five (88%) patients were receiving treatment for GD1 including 68 (72%; 68/95) patients receiving enzyme replacement therapy (imiglucerase or velaglucerase alfa) and 20 (21%; 20/95) patients receiving substrate reduction therapy (miglustat). Twenty-seven (25%) patients were splenectomized. The therapeutic goal for anemia was achieved in 105 (97%) patients. The goal for thrombocytopenia was achieved in 81 (75%) patients; this includes 24 patients who had undergone splenectomy. The goals for hepatomegaly and splenomegaly were achieved respectively in 86 of 98 (88%) and 67 of 77 (87%) patients with available data for these parameters. For the group of 95 patients receiving treatment for GD1, the numbers of patients achieving therapeutic goals were 93 (98%), 72 (76%), 77 (91%; 77/85) and 57 (86%; 57/66) for anemia, thrombocytopenia, hepatomegaly and splenomegaly respectively (see table). The number of untreated patients in the study was small (12%; 13/108); many were at therapeutic goal for the clinical parameters due to generally milder disease compared with patients receiving treatment. We found no statistically significant differences in the achievement of therapeutic goals between splenectomized patients and those with intact spleens, nor between males and females. CONCLUSIONSSpanish GD1 patients appear to have good control of hemoglobin concentration and platelet count, as well as liver and spleen volumes. The MAP Tool® can be used to standardize data collection and efficiently assess the clinical status of GD1 patients in Spain.Number of patients achieving therapeutic goals on treatment and according to splenectomy status Abstract 4963. TableTherapeutic goalAll patients n=108Patients on treatment n=95Intact spleen n=81Splenectomized n=27p-value for difference between groupsAnemia, n (%) Hemoglobin concentration ≥12 g/dL for males and ≥11 g/dL for females105 (97)93 (98)79 (98)26 (96)1.000Thrombocytopenia, n (%) (Platelet count ≥120 × 109/L)81 (75)72 (76)57 (70)24 (89)0.054Hepatomegaly, n (%) (Liver volume ≤ 1.5 multiples of normal)86 (88)a77 (91)b67 (89)c19 (83)d0.468Splenomegaly, n (%) (Spleen volume ≤ 8 multiples of normal)67 (87)e57 (86)f67 (87)eNANA Patients with available dataan=98; bn=85; cn=75; dn=23; en=77; fn=66NA, not applicable DisclosuresGiraldo:Shire: Consultancy, Research Funding; Genzyme: Consultancy, Research Funding; Actelion: Consultancy, Research Funding. Perez:Shire: Consultancy, Research Funding. Plaza:Shire: Employment. Navas:Shire: Employment.

Functional magnetic resonance imaging in narcolepsy and the kleine-levin syndrome.

This work aims at reviewing the present state of the art when it comes to understanding the pathophysiology of narcolepsy and the Kleine–Levin syndrome (KLS) from a neuroimaging point of view. This work also aims at discussing future perspectives of functional neuroimaging in these sleep disorders. We focus on functional magnetic resonance imaging (fMRI), which is a technique for in vivo measurements of brain activation in neuronal circuitries under healthy and pathological conditions. fMRI has significantly increased the knowledge on the affected neuronal circuitries in narcolepsy and the Kleine–Levin syndrome. It has been shown that narcolepsy is accompanied with disturbances of the emotional and the closely related reward systems. In the Kleine Levin syndrome, fMRI has identified hyperactivation of the thalamus as a potential biomarker that could be used in the diagnostic procedure. The fMRI findings in both narcolepsy and the Kleine–Levin syndrome are in line with previous structural and functional imaging studies. We conclude that fMRI in combination with multi-modal imaging can reveal important details about the pathophysiology in narcolepsy and the Kleine–Levin syndrome. In the future, fMRI possibly gives opportunities for diagnostic support and prediction of treatment response in individual patients.

Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification.

Despite extensive research, psychiatry remains an essentially clinical and, therefore, subjective clinical discipline, with no objective biomarkers to guide clinical practice and research. Development of psychiatric biomarkers is consequently important. A promising approach involves the use of machine learning with neuroimaging, to make predictions of diagnosis and treatment response for individual patients. Herein, we describe predictions of attention deficit hyperactivity disorder (ADHD) diagnosis using structural T(1) weighted brain scans obtained from 34 young males with ADHD and 34 controls and a support vector machine. We report 93% accuracy of individual subject diagnostic prediction. Importantly, automated selection of brain regions supporting prediction was used. High accuracy prediction was supported by a region of reduced white matter in the brainstem, associated with a pons volumetric reduction in ADHD, adjacent to the noradrenergic locus coeruleus and dopaminergic ventral tegmental area nuclei. Medications used to treat ADHD modify dopaminergic and noradrenergic function. The white matter brainstem finding raises the possibility of "catecholamine disconnection or dysregulation" contributing to the ADHD syndrome, ameliorated by medication.

Electroencephalography and analgesics

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms 'analgesics', 'electroencephalography' and 'evoked potentials' for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients.