Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BACKGROUND

The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume.

METHODS

We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors.

RESULTS

In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination.

CONCLUSIONS

Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Treatment Prediction in the ICU Using a Partitioned, Sequential, Deep Time Series Analysis

We have developed a time-oriented machine-learning tool to predict the binary decision of administering a medication and the quantitative decision regarding the specific dose. We evaluated our tool on the MIMIC-IV ICU database, for three common medical scenarios. We use an LSTM based neural network, and considerably extend its use by introducing several new concepts. We partition the common 12-hour prediction horizon into three sub-windows. Partitioning models the treatment dynamics better, and allows the use of previous sub-windows' data as additional training data with improved performance. We also introduce a sequential prediction process, composed of a binary treatment-decision model, followed, when relevant, by a quantitative dose-decision model, with improved accuracy. Finally, we examined two methods for including non-temporal features, such as age, within the temporal network. Our results provide additional treatment-prediction tools, and thus another step towards a reliable and trustworthy decision-support system that reduces the clinicians' cognitive load.

Non-animal glioblastoma models for personalized treatment

Glioblastoma is an extremely lethal cancer characterized by great heterogeneity at different molecular and cellular levels. As a result, treatment options have moved far from systemic and universal therapies toward targeted treatments and personalized medicine. However, for successful translation from preclinical studies to clinical trials, experiments must be performed on reliable disease models. Numerous experimental models have been developed for glioblastoma, ranging from simple 2D cell cultures to study the nature of the disease to complex 3D models such as neurospheres, organoids, tissue-slice cultures, bioprinted models, and tumor on chip, as perfect prototypes to evaluate the therapeutic potential of different drugs. The presence of multiple research models is consistent with the complexity and molecular diversity of glioblastoma. The advantage of such models is the recapitulation of the tumor environment, and in some cases the preservation of immune system components as well as the creation of simple vessels. There are also two case studies translating in vitro studies on glioblastoma organoids to patients as well as four ongoing clinical trials using glioblastoma models, indicating high clinical potential of glioblastoma models.

Salience and hedonic experience as predictors of central stimulant treatment response in ADHD - A resting state fMRI study

BACKGROUND

Roughly 20-30% of patients with Attention-deficit/hyperactivity disorder (ADHD) fail to respond to central stimulant (CS) medication. Genetic, neuroimaging, biochemical and behavioral biomarkers for CS response have been investigated, but currently there are no biomarkers available for clinical use that help identify CS responders and non-responders.

METHODS

In the present paper, we studied if incentive salience and hedonic experience evaluated after a single-dose CS medication could predict response and non-response to CS medication. We used a bipolar visual analogue 'wanting' and 'liking' scale to gauge incentive salience and hedonic experience in 25 healthy controls (HC) and 29 ADHD patients. HC received 30 mg methylphenidate (MPH) and ADHD patients received either MPH or lisdexamphetamine (LDX) as selected by their clinician, with dosage individually determined for optimal effect. Clinician-evaluated global impression - severity (CGI-S) and improvement (CGI-I) and patient-evaluated improvement (PGI-I) were used to assess response to CS medication. Resting state functional magnetic resonance imaging (fMRI) was conducted before and after single-dose CS to correlate wanting and liking scores to changes in functional connectivity.

RESULTS

Roughly 20% of the ADHD patients were CS non-responders (5 of 29). CS responders had significantly higher incentive salience and hedonic experience scores compared to healthy controls and CS non-responders. Resting state fMRI showed that wanting scores were significantly associated to changes in functional connectivity in ventral striatum including nucleus accumbens.

CONCLUSION

Incentive salience and hedonic experience evaluated after a single-dose CS medication segregate CS responders and non-responders, with corresponding neuroimaging biomarkers in the brain reward system.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BACKGROUND

Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.

METHODS

A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.

RESULTS

Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).

CONCLUSIONS

An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Papillary thyroid cancer organoids harboring BRAFV600E mutation reveal potentially beneficial effects of BRAF inhibitor-based combination therapies

BACKGROUNDS

Papillary thyroid cancer (PTC), which is often driven by acquired somatic mutations in BRAF genes, is the most common pathologic type of thyroid cancer. PTC has an excellent prognosis after treatment with conventional therapies such as surgical resection, thyroid hormone therapy and adjuvant radioactive iodine therapy. Unfortunately, about 20% of patients develop regional recurrence or distant metastasis, making targeted therapeutics an important treatment option. Current in vitro PTC models are limited in representing the cellular and mutational characteristics of parental tumors. A clinically relevant tool that predicts the efficacy of therapy for individuals is urgently needed.

METHODS

Surgically removed PTC tissue samples were dissociated, plated into Matrigel, and cultured to generate organoids. PTC organoids were subsequently subjected to histological analysis, DNA sequencing, and drug sensitivity assays, respectively.

RESULTS

We established 9 patient-derived PTC organoid models, 5 of which harbor BRAF^V600E mutation. These organoids have been cultured stably for more than 3 months and closely recapitulated the histological architectures as well as mutational landscapes of the respective primary tumors. Drug sensitivity assays of PTC organoid cultures demonstrated the intra- and inter-patient specific drug responses. BRAF^V600E inhibitors, vemurafenib and dabrafenib monotherapy was mildly effective in treating BRAF^V600E-mutant PTC organoids. Nevertheless, BRAF inhibitors in combination with MEK inhibitors, RTK inhibitors, or chemotherapeutic agents demonstrated improved efficacy compared to BRAF inhibition alone.

CONCLUSIONS

These data indicate that patient-derived PTC organoids may be a powerful research tool to investigate tumor biology and drug responsiveness, thus being useful to validate or discover targeted drug combinations.

Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network

PURPOSE

To predict short-term anti-vascular endothelial growth factor (anti-VEGF) treatment responder/non-responder for neovascular age-related macular degeneration (nAMD) patients based on optical coherence tomography (OCT) images.

METHODS

A total of 4944 OCT scans from 206 patients with nAMD were involved to develop and evaluate a responder/non-responder prediction method for the short-term effect of anti-VEGF therapy. A deep learning architecture named sensitive structure guided network (SSG-Net) was proposed to make the prediction leveraging a sensitive structure guidance module trained from pre- and post-treatment images. To verify its clinical efficiency, other 2 deep learning methods and 4 experienced ophthalmologists were involved to evaluate the performance of the developed model.

RESULTS

For the testing dataset, SSG-Net could predict the response by an accuracy of 84.6% and an area under the receiver curve (AUC) of 0.83, with a sensitivity of 0.692 and specificity of 1. In contrast, the 2 compared deep learning methods achieved an accuracy of 65.4% with a sensitivity of 0.461 and specificity of 0.846, and an accuracy of 73.1% with a sensitivity of 0.692 and specificity of 0.846, respectively. The predicted accuracy for 4 experienced ophthalmologists was 53.8 to 76.9%, with sensitivity of 0.538 to 0.923 and specificity of 0.385 to 0.846, respectively.

CONCLUSION

Our proposed SSG-Net shows effective prediction on the short-term efficacy of anti-VEGF treatment for nAMD patients. This technique could potentially help clinicians explain the necessity of anti-VEGF treatment to the potential responder and avoid unnecessary treatment for the non-responder.

Schema therapy versus cognitive behavioral therapy versus individual supportive therapy for depression in an inpatient and day clinic setting: study protocol of the OPTIMA-RCT

BACKGROUND

Major depressive disorder represents (MDD) a major cause of disability and disease burden. Beside antidepressant medication, psychotherapy is a key approach of treatment. Schema therapy has been shown to be effective in the treatment of psychiatric disorders, especially personality disorders, in a variety of settings and patient groups. Nevertheless, there is no evidence on its effectiveness for MDD in an inpatient nor day clinic setting and little is known about the factors that drive treatment response in such a target group.

METHODS

In the current protocol, we outline OPTIMA (OPtimized Treatment Identification at the MAx Planck Institute): a single-center randomized controlled trial of schema therapy as a treatment approach for MDD in an inpatient and day clinic setting. Over the course of 7 weeks, we compare schema therapy with cognitive behavioral therapy and individual supportive therapy, conducted in individual and group sessions and with no restrictions regarding concurrent antidepressant medication, thus approximating real-life treatment conditions. N = 300 depressed patients are included. All study therapists undergo a specific training and supervision and therapy adherence is assessed. Primary outcome is depressive symptom severity as self-assessment (Beck Depression Inventory-II) and secondary outcomes are clinical ratings of MDD (Montgomery-Asberg Depression Rating Scale), recovery rates after 7 weeks according to the Munich-Composite International Diagnostic Interview, general psychopathology (Brief Symptom Inventory), global functioning (World Health Organization Disability Assessment Schedule), and clinical parameters such as dropout rates. Further parameters on a behavioral, cognitive, psychophysiological, and biological level are measured before, during and after treatment and in 2 follow-up assessments after 6 and 24 months after end of treatment.

DISCUSSION

To our knowledge, the OPTIMA-Trial is the first to investigate the effectiveness of schema therapy as a treatment approach of MDD, to investigate mechanisms of change, and explore predictors of treatment response in an inpatient and day clinic setting by using such a wide range of parameters. Insights from OPTIMA will allow more integrative approaches of psychotherapy of MDD. Especially, the identification of intervention-specific markers of treatment response can improve evidence-based clinical decision for individualizing treatment.

TRIAL REGISTRATION

Identifier on clinicaltrials.gov : NCT03287362 ; September, 12, 2017.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

Distinct striatum pathways connected to salience network predict symptoms improvement and resilient functioning in schizophrenia following risperidone monotherapy

Abnormal interactions between the striatum and salience network (SN) are considered as etiological and treatment-sensitive marker in schizophrenia. However, whether alterations in the intrinsic dynamics as reflected by resting-state functional connectivity (RSFC) between the striatum and salience network may predict treatment response to the widely used antipsychotic treatment strategies (risperidone, monotherapy) has not been examined systematically. To this end, treatment-naive first-episode schizophrenia patients (n = 41) underwent task-free resting-state fMRI assessment before (baseline) and after 8 weeks of risperidone monotherapy (n = 38). Intrinsic connectivity between striatal sub-regions and core salience processing nodes were examined and compared to carefully matched healthy controls (HC) to determine disorder-specific and treatment-predictive neural markers. Findings demonstrate hypo-connectivity of both ventral and dorsal striatal-SN pathways in patients at baseline. Importantly, specifically the dorsal striatal pathway at baseline could predict negative symptoms improvement in patients; while ventral striatal pathways could predict positive symptoms improvement. Together, results indicate that distinct striatal-SN pathways represent specific treatment-success markers for the effects of risperidone, suggesting that alterations in dorsal versus ventral striatal network markers may represent brain-based markers for specific symptomatologic improvements following risperidone mono-therapy.

Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to

The number of publications on mathematical modeling of cancer is growing at an exponential rate, according to PubMed records, provided by the US National Library of Medicine and the National Institutes of Health. Seminal papers have initiated and promoted mathematical modeling of cancer and have helped define the field of mathematical oncology (Norton and Simon in J Natl Cancer Inst 58:1735-1741, 1977; Norton in Can Res 48:7067-7071, 1988; Hahnfeldt et al. in Can Res 59:4770-4775, 1999; Anderson et al. in Comput Math Methods Med 2:129-154, 2000. https://doi.org/10.1080/10273660008833042 ; Michor et al. in Nature 435:1267-1270, 2005. https://doi.org/10.1038/nature03669 ; Anderson et al. in Cell 127:905-915, 2006. https://doi.org/10.1016/j.cell.2006.09.042 ; Benzekry et al. in PLoS Comput Biol 10:e1003800, 2014. https://doi.org/10.1371/journal.pcbi.1003800 ). Following the introduction of undergraduate and graduate programs in mathematical biology, we have begun to see curricula developing with specific and exclusive focus on mathematical oncology. In 2018, 218 articles on mathematical modeling of cancer were published in various journals, including not only traditional modeling journals like the Bulletin of Mathematical Biology and the Journal of Theoretical Biology, but also publications in renowned science, biology, and cancer journals with tremendous impact in the cancer field (Cell, Cancer Research, Clinical Cancer Research, Cancer Discovery, Scientific Reports, PNAS, PLoS Biology, Nature Communications, eLife, etc). This shows the breadth of cancer models that are being developed for multiple purposes. While some models are phenomenological in nature following a bottom-up approach, other models are more top-down data-driven. Here, we discuss the emerging trend in mathematical oncology publications to predict novel, optimal, sometimes even patient-specific treatments, and propose a convention when to use a model to predict novel treatments and, probably more importantly, when not to.

Computational analysis of receptor tyrosine kinase inhibitors and cancer metabolism: implications for treatment and discovery of potential therapeutic signatures

Background

Receptor tyrosine kinase (RTK) inhibitors are frequently used to treat cancers and the results have been mixed, some of these small molecule drugs are highly successful while others show a more modest response. A high number of studies have been conducted to investigate the signaling mechanisms and corresponding therapeutic influence of RTK inhibitors in order to explore the therapeutic potential of RTK inhibitors. However, most of these studies neglected the potential metabolic impact of RTK inhibitors, which could be highly associated with drug efficacy and adverse effects during treatment.

Methods

In order to fill these knowledge gaps and improve the therapeutic utilization of RTK inhibitors a large-scale computational simulation/analysis over multiple types of cancers with the treatment responses of RTK inhibitors was performed. The pharmacological data of all eight RTK inhibitor and gene expression profiles of 479 cell lines from The Cancer Cell Line Encyclopedia were used.

Results

The potential metabolic impact of RTK inhibitors on different types of cancers were analyzed resulting in cancer-specific (breast, liver, pancreas, central nervous system) metabolic signatures. Many of these are in line with results from different independent studies, thereby providing indirect verification of the obtained results.

Conclusions

Our study demonstrates the potential of using a computational approach on signature-based-analysis over multiple cancer types. The results reveal the strength of multiple-cancer analysis over conventional signature-based analysis on a single cancer type.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5804-0) contains supplementary material, which is available to authorized users.

Rostral Anterior Cingulate Cortex Morphology Predicts Treatment Response to Internet-Based Cognitive Behavioral Therapy for Depression

BACKGROUND

Rostral and subgenual anterior cingulate cortex (rACC and sgACC) activity and, to a lesser extent, volume have been shown to predict depressive symptom improvement across different antidepressant treatments. This study extends prior work by examining whether rACC and/or sgACC morphology predicts treatment response to Internet-based cognitive behavioral therapy (iCBT) for major depressive disorder. This is the first study to examine neural predictors of response to iCBT.

METHODS

Hierarchical linear modeling tested whether pretreatment rACC and sgACC volumes predicted depressive symptom improvement during a six-session (10-week) randomized clinical trial of iCBT (n = 35) versus a monitored attention control condition (n = 38). Analyses also tested whether pretreatment rACC and sgACC volumes differed between patients who achieved depression remission versus patients who did not remit.

RESULTS

Larger pretreatment right rACC volume was a significant predictor of greater depressive symptom improvement in iCBT even when controlling for demographic (age, gender, race) and clinical (baseline depression, anhedonia, and anxiety) variables previously linked to treatment response. In addition, pretreatment right rACC volume was larger among patients receiving iCBT whose depression eventually remitted relative to patients who did not remit. Corresponding analyses in the monitored attention control group and for the sgACC were not significant.

CONCLUSIONS

rACC volume before iCBT demonstrated incremental predictive validity beyond clinical and demographic variables previously found to predict symptom improvement. Such findings may help inform our understanding of the mediating anatomy of iCBT and, if replicated, may suggest neural targets to augment treatment response (e.g., via modulation of rACC function).

Neural responses to social threat and predictors of cognitive behavioral therapy and acceptance and commitment therapy in social anxiety disorder

Previous research has often highlighted hyperactivity in emotion regions to simple, static social threat cues in social anxiety disorder (SAD). Investigation of the neurobiology of SAD using more naturalistic paradigms can further reveal underlying mechanisms and how these relate to clinical outcomes. We used fMRI to investigate responses to novel dynamic rejection stimuli in individuals with SAD (N=70) and healthy controls (HC; N=17), and whether these responses predicted treatment outcomes following cognitive behavioral therapy (CBT) or acceptance and commitment therapy (ACT). Both HC and SAD groups reported greater distress to rejection compared to neutral social stimuli. At the neural level, HCs exhibited greater activations in social pain/rejection regions, including dorsal anterior cingulate cortex and anterior insula, to rejection stimuli. The SAD group evidenced a different pattern, with no differences in these rejection regions and relatively greater activations in the amygdala and other regions to neutral stimuli. Greater responses in anterior cingulate cortex and the amygdala to rejection vs. neutral stimuli predicted better CBT outcomes. In contrast, enhanced activity in sensory-focused posterior insula predicted ACT responses.

Neural markers of attention to aversive pictures predict response to cognitive behavioral therapy in anxiety and depression

Excessive attention toward aversive information may be a core mechanism underlying emotional disorders, but little is known about whether this is predictive of response to treatments. We evaluated whether enhanced attention toward aversive stimuli, as indexed by an event-related potential component, the late positive potential (LPP), would predict response to cognitive behavioral therapy (CBT) in patients with social anxiety disorder and/or major depressive disorder. Thirty-two patients receiving 12 weeks of CBT responded to briefly-presented pairs of aversive and neutral pictures that served as targets or distracters while electroencephaolography was recorded. Patients with larger pre-treatment LPPs to aversive relative to neutral distracters (when targets were aversive) were more likely to respond to CBT, and demonstrated larger reductions in symptoms of depression and anxiety following treatment. Increased attention toward irrelevant aversive stimuli may signal attenuated top-down control, so treatments like CBT that improve this control could be beneficial for these individuals.

Whole-genome expression analysis reveals genes associated with treatment response to escitalopram in major depression

The reasons for variability in treatment response in major depressive disorder (MDD) are not fully understood, but there is accumulating evidence suggesting that therapeutic outcomes of antidepressants can be influenced by genetic factors. In the present study we applied the microarray Illumina platform for whole genome expression profiling in depressive patients treated with escitalopram medication in order to identify genes underlying response to antidepressant treatment. The initial study sample consisted of 135 outpatients with major depressive disorder (mean age 31.1±11.6 years, 68% females) treated with escitalopram 10-20mg/day for 12 weeks, from which 87 patients (55 females) were included in gene expression analyzing. The gene expression profiles were measured on peripheral blood cells at baseline, at week 4 and at the end of treatment (week 12) using BeadChips Illumina. The fold change was used to demonstrate rate of changes in average gene expressions between studied groups. Statistical analyses were performed using the false discovery rate (FDR). The most interesting gene, which showed the predictive effect on treatment outcome by delineating low dose responders and treatment-resistant patients at the beginning of medication, was NLGN2, belonging to a family of neuronal cell surface proteins and involving in synapse formation. In addition, the several gene clusters, related to immune response, signal transduction and neurotrophin pathway, have distinguished responders from non-responders at the week 4 of treatment. After 4 weeks of escitalopram treatment (10mg/day), the YWHAZ gene has showed the highest transcriptional change in responders as compared with non-responders. Finally, at the end of the treatment we noticed that at least three genes (NR2C2, ZNF641, FKBP1A) have been strongly associated with resistance to escitalopram. Thus the results of this study support that exploration of peripheral gene expression is a useful tool in the further identification of novel genetic biomarkers for antidepressant treatment response.

Use of intravoxel incoherent motion diffusion-weighted MR imaging for assessment of treatment response to invasive fungal infection in the lung

OBJECTIVES

The purpose of this study was to determine whether intravoxel incoherent motion (IVIM) -derived parameters and apparent diffusion coefficient (ADC) could act as imaging biomarkers for predicting antifungal treatment response.

METHODS

Forty-six consecutive patients (mean age, 33.9 ± 13.0 y) with newly diagnosed invasive fungal infection (IFI) in the lung according to EORTC/MSG criteria were prospectively enrolled. All patients underwent diffusion-weighted magnetic resonance (MR) imaging at 3.0 T using 11 b values (0-1000 sec/mm²). ADC, pseudodiffusion coffiecient D*, perfusion fraction f, and the diffusion coefficient D were compared between patients with favourable (n=32) and unfavourable response (n=14).

RESULTS

f values were significantly lower in the unfavourable response group (12.6%±4.4%) than in the favourable response group (30.2%±8.6%) (Z=4.989, P<0.001). However, the ADC, D, and D* were not significantly different between the two groups (P>0.05). Receiver operating characteristic curve analyses showed f to be a significant predictor for differentiation, with a sensitivity of 93.8% and a specificity of 92.9%.

CONCLUSIONS

IVIM-MRI is potentially useful in the prediction of antifungal treatment response to patients with IFI in the lung. Our results indicate that a low perfusion fraction f may be a noninvasive imaging biomarker for unfavourable response.

KEY POINTS

• Recognition of IFI indicating clinical outcome is important for treatment decision-making. • IVIM can reflect diffusion and perfusion information of IFI lesions separately. • Perfusion characteristics of IFI lesions could help differentiate treatment response. • An initial low f may predict unfavourable response in IFI.

Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report

It is essential to improve antidepressant treatment of major depressive disorder (MDD) and one way this could be achieved is by reducing the number of treatment steps by employing biomarkers that can predict treatment outcome. This study investigated differences between MDD patients and healthy controls in the P3 and N1 component from the event-related potential (ERP) generated in a standard two-tone oddball paradigm. Furthermore, the P3 and N1 are investigated as predictors for treatment outcome to three different antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D)--a multi-center, international, randomized, prospective practical trial--1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression. P3 and N1 latencies and amplitudes were analyzed using a peak-picking approach and further replicated by using exact low resolution tomography (eLORETA). A reduced P3 was found in MDD patients compared to controls by a peak-picking analysis. This was validated in a temporal global field power analysis. Source density analysis revealed that the difference in cortical activity originated from the posterior cingulate and parahippocampal gyrus. Male non-responders to venlafaxine-XR had significantly smaller N1 amplitudes than responders. This was demonstrated by both analytical methods. Male non-responders to venlafaxine-XR had less activity originating from the left insular cortex. The observed results are discussed from a neural network viewpoint.