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Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy-to-use multi-channel near-infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high-accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker-based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub-second, multivariate temporal patterns of BOLD responses and high-accuracy predictions based on low-cost, easy-to-use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Ziele: Das Ziel dieser offiziellen Leitlinie, die von der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe (DGGG) und der Deutschen Krebsgesellschaft (DKG) publiziert und koordiniert wurde, ist es, die Früherkennung, Diagnostik, Therapie und Nachsorge des Mammakarzinoms zu optimieren.
Methoden: Der Aktualisierungsprozess der S3-Leitlinie aus 2012 basierte zum einen auf der Adaptation identifizierter Quellleitlinien und zum anderen auf Evidenzübersichten, die nach Entwicklung von PICO-(Patients/Interventions/Control/Outcome-)Fragen, systematischer Recherche in Literaturdatenbanken sowie Selektion und Bewertung der gefundenen Literatur angefertigt wurden. In den interdisziplinären Arbeitsgruppen wurden auf dieser Grundlage Vorschläge für Empfehlungen und Statements erarbeitet, die im Rahmen von strukturierten Konsensusverfahren modifiziert und graduiert wurden.
Empfehlungen: Der Teil 1 dieser Kurzversion der Leitlinie zeigt Empfehlungen zur Früherkennung, Diagnostik und Nachsorge des Mammakarzinoms: Der Stellenwert des Mammografie-Screenings wird in der aktualisierten Leitlinienversion bestätigt und bildet damit die Grundlage der Früherkennung. Neben den konventionellen Methoden der Karzinomdiagnostik wird die Computertomografie (CT) zum Staging bei höherem Rückfallrisiko empfohlen. Die Nachsorgekonzepte beinhalten Untersuchungsintervalle für die körperliche Untersuchung, Ultraschall und Mammografie, während weiterführende Gerätediagnostik und Tumormarkerbestimmungen bei der metastasierten Erkrankung Anwendung finden.
Purpose: The aim of this official guideline coordinated and published by the German Society for Gynecology and Obstetrics (DGGG) and the German Cancer Society (DKG) was to optimize the screening, diagnosis, therapy and follow-up care of breast cancer.
Methods: The process of updating the S3 guideline dating from 2012 was based on the adaptation of identified source guidelines which were combined with reviews of evidence compiled using PICO (Patients/Interventions/Control/Outcome) questions and the results of a systematic search of literature databases and the selection and evaluation of the identified literature. The interdisciplinary working groups took the identified materials as their starting point to develop recommendations and statements which were modified and graded in a structured consensus procedure.
Recommendations: Part 1 of this short version of the guideline presents recommendations for the screening, diagnosis and follow-up care of breast cancer. The importance of mammography for screening is confirmed in this updated version of the guideline and forms the basis for all screening. In addition to the conventional methods used to diagnose breast cancer, computed tomography (CT) is recommended for staging in women with a higher risk of recurrence. The follow-up concept includes suggested intervals between physical, ultrasound and mammography examinations, additional high-tech diagnostic procedures, and the determination of tumor markers for the evaluation of metastatic disease.
Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).
Hypercholesterolemia and elevated homocysteine concentrations are associated with cardiovascular risk. Previous studies have demonstrated a cholesterol-lowering effect of red yeast rice (RYR) supplements which contained 5 to 10 mg of monacolin K. We hypothesized that the intake of a low monacolin K dose may likewise reduce low-density lipoprotein-cholesterol (LDL-C) and other plasma lipids. In secondary analyses, we tested the homocysteine lowering effect of folic acid, which was also included in the study preparation. Therefore, we conducted a randomized, double-blind, and placebo-controlled intervention study. One hundred forty-two nonstatin-treated participants with hypercholesterolemia (LDL-C ≥ 4.14 ≤ 5.69 mmol/L) were randomized to the supplement group with RYR or the placebo group. Participants of the supplement group consumed 3 mg monacolin K and 200 μg folic acid per day. A significant (P < .001) reduction of LDL-C (-14.8%), total cholesterol (-11.2%), and homocysteine (-12.5%) was determined in the supplement group after 12 weeks. A total of 51% of the participants treated with RYR achieved the limit of LDL-C <4.14 mmol/L advised and 26% reached the threshold level of homocysteine <10 μmol/L. No significant changes were exhibited within the placebo group. Other parameters remained unchanged and no intolerances or serious adverse events were observed. In conclusion, we demonstrated that a low dose of daily 3 mg monacolin K from RYR reduces the concentration of LDL-C; a risk factor for cardiovascular diseases.
One limitation of mechanical thrombectomy (MT) is clot migration during procedure. This might be caused by abruption of the trapped thrombus at the distal access catheter (DAC) tip during stent-retriever retraction due to the cylindrical shaped tip of the DAC. Aiming to solve this problem, this study evaluates the proof-of-concept of a new designed funnel-shaped tip, in an experimental in vitro setting. Two catheter models, one with a funnel-shaped tip and one with a cylindrical-shaped tip, were compared in an experimental setup. For MT a self-made vessel model and thrombi generated from pig’s blood were used. MT was performed 20 times for each device using two different stent-retrievers, 10 times respectively. For the funnel-shaped model: for both stent-retrievers (Trevo XP ProVue 3/20 mm; Trevo XP ProVue 4/20 mm) MT was successful at first pass in 9/10 (90%), respectively. For the cylindrical-shaped model: MT was successful at first pass in 5/10 (50%) with the smaller stent-retriever and in 6/10 (60%) with the larger stent-retriever. The experiments show a better recanalization rate for funnel-shaped tips, than for cylindrical-shaped tips. These results are indicating a good feasibility for this new approach, thus the development of a prototype catheter seems reasonable.
An integrative correlation of myopathology, phenotype and genotype in late onset Pompe disease
(2019)
Aims: Pompe disease is caused by pathogenic mutations in the alpha 1,4‐glucosidase (GAA) gene and in patients with late onset Pome disease (LOPD), genotype–phenotype correlations are unpredictable. Skeletal muscle pathology includes glycogen accumulation and altered autophagy of various degrees. A correlation of the muscle morphology with clinical features and the genetic background in GAA may contribute to the understanding of the phenotypic variability.
Methods: Muscle biopsies taken before enzyme replacement therapy were analysed from 53 patients with LOPD. On resin sections, glycogen accumulation, fibrosis, autophagic vacuoles and the degree of muscle damage (morphology‐score) were analysed and the results were compared with clinical findings. Additional autophagy markers microtubule‐associated protein 1A/1B‐light chain 3, p62 and Bcl2‐associated athanogene 3 were analysed on cryosections from 22 LOPD biopsies.
Results: The myopathology showed a high variability with, in most patients, a moderate glycogen accumulation and a low morphology‐score. High morphology‐scores were associated with increased fibrosis and autophagy highlighting the role of autophagy in severe stages of skeletal muscle damage. The morphology‐score did not correlate with the patient's age at biopsy, disease duration, nor with the residual GAA enzyme activity or creatine‐kinase levels. In 37 patients with LOPD, genetic analysis identified the most frequent mutation, c.‐32‐13T>G, in 95%, most commonly in combination with c.525delT (19%). No significant correlation was found between the different GAA genotypes and muscle morphology type.
Conclusions: Muscle morphology in LOPD patients shows a high variability with, in most cases, moderate pathology. Increased pathology is associated with more fibrosis and autophagy.
The Behavioral Inhibition System (BIS) as defined within the Reinforcement Sensitivity Theory (RST) modulates reactions to stimuli indicating aversive events. Gray’s trait Anxiety determines the extent to which stimuli activate the BIS. While studies have identified the amygdala-septo-hippocampal circuit as the key-neural substrate of this system in recent years and measures of resting-state dynamics such as randomness and local synchronization of spontaneous BOLD fluctuations have recently been linked to personality traits, the relation between resting-state dynamics and the BIS remains unexplored. In the present study, we thus examined the local synchronization of spontaneous fMRI BOLD fluctuations as measured by Regional Homogeneity (ReHo) in the hippocampus and the amygdala in twenty-seven healthy subjects. Correlation analyses showed that Gray’s trait Anxiety was significantly associated with mean ReHo in both the amygdala and the hippocampus. Specifically, Gray’s trait Anxiety explained 23% and 17% of resting-state ReHo variance in the left amygdala and the left hippocampus, respectively. In summary, we found individual differences in Gray’s trait Anxiety to be associated with ReHo in areas previously associated with BIS functioning. Specifically, higher ReHo in resting-state neural dynamics corresponded to lower sensitivity to punishment scores both in the amygdala and the hippocampus. These findings corroborate and extend recent findings relating resting-state dynamics and personality while providing first evidence linking properties of resting-state fluctuations to Gray’s BIS.
Background: Tuberous sclerosis complex (TSC), a multisystem genetic disorder, affects many organs and systems, characterized by benign growths. This German multicenter study estimated the disease-specific costs and cost-driving factors associated with various organ manifestations in TSC patients. Methods: A validated, three-month, retrospective questionnaire was administered to assess the sociodemographic and clinical characteristics, organ manifestations, direct, indirect, out-of-pocket, and nursing care-level costs, completed by caregivers of patients with TSC throughout Germany. Results: The caregivers of 184 patients (mean age 9.8 ± 5.3 years, range 0.7–21.8 years) submitted questionnaires. The reported TSC disease manifestations included epilepsy (92%), skin disorders (86%), structural brain disorders (83%), heart and circulatory system disorders (67%), kidney and urinary tract disorders (53%), and psychiatric disorders (51%). Genetic variations in TSC2 were reported in 46% of patients, whereas 14% were reported in TSC1. Mean total direct health care costs were EUR 4949 [95% confidence interval (95% CI) EUR 4088–5863, median EUR 2062] per patient over three months. Medication costs represented the largest direct cost category (54% of total direct costs, mean EUR 2658), with mechanistic target of rapamycin (mTOR) inhibitors representing the largest share (47%, EUR 2309). The cost of anti-seizure drugs (ASDs) accounted for a mean of only EUR 260 (5%). Inpatient costs (21%, EUR 1027) and ancillary therapy costs (8%, EUR 407) were also important direct cost components. The mean nursing care-level costs were EUR 1163 (95% CI EUR 1027–1314, median EUR 1635) over three months. Total indirect costs totaled a mean of EUR 2813 (95% CI EUR 2221–3394, median EUR 215) for mothers and EUR 372 (95% CI EUR 193–586, median EUR 0) for fathers. Multiple regression analyses revealed polytherapy with two or more ASDs and the use of mTOR inhibitors as independent cost-driving factors of total direct costs. Disability and psychiatric disease were independent cost-driving factors for total indirect costs as well as for nursing care-level costs. Conclusions: This study revealed substantial direct (including medication), nursing care-level, and indirect costs associated with TSC over three months, highlighting the spectrum of organ manifestations and their treatment needs in the German healthcare setting.
Characterization of a dual BET/HDAC inhibitor for treatment of pancreatic ductal adenocarcinoma
(2020)
Pancreatic ductal adenocarcinoma (PDAC) is resistant to virtually all chemo‐ and targeted therapeutic approaches. Epigenetic regulators represent a novel class of drug targets. Among them, BET and HDAC proteins are central regulators of chromatin structure and transcription, and preclinical evidence suggests effectiveness of combined BET and HDAC inhibition in PDAC. Here, we describe that TW9, a newly generated adduct of the BET inhibitor (+)‐JQ1 and class I HDAC inhibitor CI994, is a potent dual inhibitor simultaneously targeting BET and HDAC proteins. TW9 has a similar affinity to BRD4 bromodomains as (+)‐JQ1 and shares a conserved binding mode, but is significantly more active in inhibiting HDAC1 compared to the parental HDAC inhibitor CI994. TW9 was more potent in inhibiting tumor cell proliferation compared to (+)‐JQ1, CI994 alone or combined treatment of both inhibitors. Sequential administration of gemcitabine and TW9 showed additional synergistic antitumor effects. Microarray analysis revealed that dysregulation of a FOSL1‐directed transcriptional program contributed to the antitumor effects of TW9. Our results demonstrate the potential of a dual chromatin‐targeting strategy in the treatment of PDAC and provide a rationale for further development of multitarget inhibitors.
Background: The approval of everolimus (EVE) for the treatment of angiomyolipoma (2013), subependymal giant cell astrocytoma (2013) and drug-refractory epilepsy (2017) in patients with tuberous sclerosis complex (TSC) represents the first disease-modifying treatment option available for this rare and complex genetic disorder. Objective: The objective of this study was to analyse the use, efficacy, tolerability and treatment retention of EVE in patients with TSC in Germany from the patient’s perspective. Methods: A structured cross-age survey was conducted at 26 specialised TSC centres in Germany and by the German TSC patient advocacy group between February and July 2019, enrolling children, adolescents and adult patients with TSC. Results: Of 365 participants, 36.7% (n = 134) reported the current or past intake of EVE, including 31.5% (n = 115) who were taking EVE at study entry. The mean EVE dosage was 6.1 ± 2.9 mg/m2 (median: 5.6 mg/m2, range 2.0–15.1 mg/m2) in children and adolescents and 4 ± 2.1 mg/m2 (median: 3.7 mg/m2, range 0.8–10.1 mg/m2) in adult patients. An early diagnosis of TSC, the presence of angiomyolipoma, drug-refractory epilepsy, neuropsychiatric manifestations, subependymal giant cell astrocytoma, cardiac rhabdomyoma and overall multi-organ involvement were associated with the use of EVE as a disease-modifying treatment. The reported efficacy was 64.0% for angiomyolipoma (75% in adult patients), 66.2% for drug-refractory epilepsy, and 54.4% for subependymal giant cell astrocytoma. The overall retention rate for EVE was 85.8%. The retention rates after 12 months of EVE therapy were higher among adults (93.7%) than among children and adolescents (88.7%; 90.5% vs 77.4% after 24 months; 87.3% vs 77.4% after 36 months). Tolerability was acceptable, with 70.9% of patients overall reporting adverse events, including stomatitis (47.0%), acne-like rash (7.7%), increased susceptibility to common infections and lymphoedema (each 6.0%), which were the most frequently reported symptoms. With a total score of 41.7 compared with 36.8 among patients not taking EVE, patients currently being treated with EVE showed an increased Liverpool Adverse Event Profile. Noticeable deviations in the sub-items ‘tiredness’, ‘skin problems’ and ‘mouth/gum problems’, which are likely related to EVE-typical adverse effects, were more frequently reported among patients taking EVE. Conclusions: From the patients’ perspective, EVE is an effective and relatively well-tolerated disease-modifying treatment option for children, adolescents and adults with TSC, associated with a high long-term retention rate that can be individually considered for each patient. Everolimus therapy should ideally be supervised by a centre experienced in the use of mechanistic target of rapamycin inhibitors, and adverse effects should be monitored on a regular basis.
Functional near-infrared spectroscopy (fNIRS) is an established optical neuroimaging method for measuring functional hemodynamic responses to infer neural activation. However, the impact of individual anatomy on the sensitivity of fNIRS measuring hemodynamics within cortical gray matter is still unknown. By means of Monte Carlo simulations and structural MRI of 23 healthy subjects (mean age: (25.0 +- 2.8 years), we characterized the individual distribution of tissue-specific NIR-light absorption underneath 24 prefrontal fNIRS channels. We, thereby, investigated the impact of scalp-cortex distance (SCD), frontal sinus volume as well as sulcal morphology on gray matter volumes (V gray) traversed by NIR-light, i.e. anatomy-dependent fNIRS sensitivity. The NIR-light absorption between optodes was distributed describing a rotational ellipsoid with a mean penetration depth of (23.6 +- 0.7 mm) considering the deepest 5% of light. Of the detected photon packages scalp and bone absorbed (96.4 +- 9.7)% and absorbed (3,1 +- 1.8)% of the energy. The mean V gray volume (1.1 +- 0.4)cm 3 was negatively correlated (r = -.76) with the SCD and frontal sinus volume (r= -.57) and was reduced by in subjects with relatively large compared to small frontal sinus. Head circumference was significantly positively correlated with the mean SCD (r= .46) and the traversed frontal sinus volume (r= .43). Sulcal morphology had no significant impact on . Our findings suggest to consider individual SCD and frontal sinus volume as anatomical factors impacting fNIRS sensitivity. Head circumference may represent a practical measure to partly control for these sources of error variance.
This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, |GE | and |GM|, using the ¯pp → μ+μ− reaction at PANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at PANDA, using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is ¯pp → π+π−,due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distribuations of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented.
Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
Introduction: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available.
Material and Methods: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD.
Results: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%.
Discussion: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.
AKTIVA-MCI is a program for patients with mild cognitive impairment (MCI) that aims to enhance participation in cognitively stimulating leisure activities. Participation in cognitively stimulating activities seems to be a potential strategy for people with MCI delaying cognitive decline for a while. In total, 35 MCI patients were enrolled in the pilot study of whom 29 completed the whole program (16 female, 71.1±7.5 years; Mini Mental Status Examination score: 28±2.2). Daily activity protocols were used to measure the frequency of participation in cognitively stimulating activities during the program (12 sessions). Additional standardized psychometric tests and questionnaires were used to assess cognition, mood, and subjective memory decline. Analyses of the daily activity protocols showed that during the intervention participants increased the frequency of several cognitively stimulating leisure activities. Comparison of pre-post data indicates no changes in cognitive status, mood, and subjective memory decline. These findings indicate that the program is suitable for patients with MCI.
In healthy older adults, resveratrol supplementation has been shown to improve long-term glucose control, resting-state functional connectivity (RSFC) of the hippocampus, and memory function. Here, we aimed to investigate if these beneficial effects extend to individuals at high-risk for dementia, i.e., patients with mild cognitive impairment (MCI). In a randomized, double-blind interventional study, 40 well-characterized patients with MCI (21 females; 50–80 years) completed 26 weeks of resveratrol (200 mg/d; n = 18) or placebo (1,015 mg/d olive oil; n = 22) intake. Serum levels of glucose, glycated hemoglobin A1c and insulin were determined before and after intervention. Moreover, cerebral magnetic resonance imaging (MRI) (3T) (n = 14 vs. 16) was conducted to analyze hippocampus volume, microstructure and RSFC, and neuropsychological testing was conducted to assess learning and memory (primary endpoint) at both time points. In comparison to the control group, resveratrol supplementation resulted in lower glycated hemoglobin A1c concentration with a moderate effect size (ANOVARM p = 0.059, Cohen's d = 0.66), higher RSFC between right anterior hippocampus and right angular cortex (p < 0.001), and led to a moderate preservation of left anterior hippocampus volume (ANOVARM p = 0.061, Cohen's d = 0.68). No significant differences in memory performance emerged between groups. This proof-of-concept study indicates for the first-time that resveratrol intake may reduce glycated hemoglobin A1c, preserves hippocampus volume, and improves hippocampus RSFC in at-risk patients for dementia. Larger trials with longer intervention time should now determine if these benefits can be validated and extended to cognitive function.
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.