***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: OBJECTIVE The aim of this work is to provide a supervised method to assist the diagnosis and monitor the progression of the Alzheimer 's disease ( AD ) using information which can be extracted from a functional magnetic resonance imaging ( fMRI ) experiment . METHODS AND MATERIAL S The proposed method consists of five stages : ( a ) preprocessing of fMRI data , ( b ) modeling of the fMRI voxel time series using a generalized linear model , ( c ) feature extraction from the fMRI experiment , ( d ) feature selection , and ( e ) classification using the r and om forests algorithm . In the last stage we employ features that were extracted from the fMRI and other features such as demographics , behavioral and volumetric measures . The aim of the classification is twofold : first to diagnose AD and second to classify AD as very mild and mild . RESULTS The method is evaluated using data from 41 subjects . The stage of AD is established using the Washington University Alzheimer 's Disease Research Center recruitment and assessment procedures . The method classifies a patient as healthy or demented with 84 % sensitivity and 92.3 % specificity , and the stages of AD with 81 % and 87 % accuracy for the three class and the four class problem , respectively . CONCLUSIONS The method is advantageous since it is fully automated and for the first time the diagnosis and staging of the disease are addressed using fMRI Individuals with developmental dyslexia vary in their ability to improve reading skills , but the brain basis for improvement remains largely unknown . We performed a prospect i ve , longitudinal study over 2.5 y in children with dyslexia ( n = 25 ) or without dyslexia ( n = 20 ) to discover whether initial behavioral or brain measures , including functional MRI ( fMRI ) and diffusion tensor imaging ( DTI ) , can predict future long-term reading gains in dyslexia . No behavioral measure , including widely used and st and ardized reading and language tests , reliably predicted future reading gains in dyslexia . Greater right prefrontal activation during a reading task that dem and ed phonological awareness and right superior longitudinal fasciculus ( including arcuate fasciculus ) white-matter organization significantly predicted future reading gains in dyslexia . Multivariate pattern analysis ( MVPA ) of these two brain measures , using linear support vector machine ( SVM ) and cross-validation , predicted significantly above chance ( 72 % accuracy ) which particular child would or would not improve reading skills ( behavioral measures were at chance ) . MVPA of whole-brain activation pattern during phonological processing predicted which children with dyslexia would improve reading skills 2.5 y later with > 90 % accuracy . These findings identify right prefrontal brain mechanisms that may be critical for reading improvement in dyslexia and that may differ from typical reading development . Brain measures that predict future behavioral outcomes ( neuroprognosis ) may be more accurate , in some cases , than available behavioral measures Successful automated diagnoses of attention deficit hyperactive disorder ( ADHD ) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease . In this work , we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research . We created a prediction model using the l and mark ADHD 200 data set focusing on resting state functional connectivity ( rs-fc ) and structural brain imaging . We predicted ADHD status and subtype , obtained by behavioral examination , using imaging data , intelligence quotients and other covariates . The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data , including the use of singular value decompositions ( SVDs ) , CUR decompositions , r and om forest , gradient boosting , bagging , voxel-based morphometry , and support vector machines as well as important insights into the value , and potentially lack thereof , of imaging biomarkers of disease . The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and r and om forest algorithm . We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion . Of note , a byproduct of this research is a potential automated method for detecting subtle in-scanner motion . The final prediction algorithm , a weighted combination of several algorithms , had an external test set specificity of 94 % with sensitivity of 21 % . The most promising imaging biomarker was a correlation graph from a motor network parcellation . In summary , we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set . The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD Functional magnetic resonance imaging ( fMRI ) of default mode network ( DMN ) brain activity during resting is recently gaining attention as a potential noninvasive biomarker to diagnose incipient Alzheimer 's disease . The aim of this study was to determine which method of data processing provides highest diagnostic power and to define metrics to further optimize the diagnostic value . fMRI was acquired in 21 healthy subjects , 17 subjects with mild cognitive impairment and 15 patients with Alzheimer 's disease ( AD ) and data evaluated both with volumes of interest (VOI)-based signal time course evaluations and independent component analyses ( ICA ) . The first approach determines the amount of DMN region interconnectivity ( as expressed with correlation coefficients ) ; the second method determines the magnitude of DMN coactivation .  Apolipoprotein E ( ApoE ) genotyping was available in 41 of the subjects examined . Diagnostic power ( expressed as accuracy ) of data of a single DMN region in independent component analyses was 64 % , that of a single correlation of time courses between 2 DMN regions was 71 % , respectively . With multivariate analyses combining both methods of analysis and data from various regions , accuracy could be increased to 97 % ( sensitivity 100 % , specificity 95 % ) . In nondemented subjects , no significant differences in activity within DMN could be detected comparing ApoE ε4 allele carriers and ApoE ε4 allele noncarriers . However , there were some indications that fMRI might yield useful information given a larger sample . Time course correlation analyses seem to outperform independent component analyses in the identification of patients with Alzheimer 's disease . However , multivariate analyses combining both methods of analysis by considering the activity of various parts of the DMN as well as the interconnectivity between these regions are required to achieve optimal and clinical ly acceptable diagnostic power The functional neuroanatomy of verbal working memory is a potential diagnostic biomarker for depression . Twenty patients with unipolar depression and 20 healthy controls performed a variable load version ( n-back ) of the task . Functional MRI data were analysed with support vector machine methods . Diagnostic classification was highest at the mid-level of task difficulty ( 2-back ) ( sensitivity 65 % , specificity 70 % , P<0.009 ) . Significant classification of clinical response ( ≥50 % reduction in clinical symptom ratings ) was found at the most difficult level ( 3-back ) ( sensitivity 85 % , specificity 52 % , P<0.003 ) . The functional neuroanatomy of verbal working memory provides a statistically significant but clinical ly moderate contribution as a diagnostic biomarker for depression , whereas its potential as a neural predictor of clinical response requires further investigation This paper presents a discriminative model of multivariate pattern classification , based on functional magnetic resonance imaging ( fMRI ) and anatomical template . As a measure of brain function , Regional homogeneity ( ReHo ) is calculated voxel by voxel , and then a widely used anatomical template is applied on ReHo map to parcelate it into 116 brain regions . The mean and st and ard deviation of ReHo values in each region are extracted as features . Pseudo-Fisher Linear Discriminant Analysis ( PFLDA ) is performed for training sample s to generate discriminative model . Classification experiments have been carried out in 48 schizophrenia patients and 35 normal controls . Under a full leave-one-out ( LOO ) cross-validation , correct prediction rate of 80 % is achieved . Anatomical parcellation process is proved useful to improve classification rate by a control experiment . The discriminative model shows its ability to reveal abnormal brain functional activities and identify people with schizophrenia Stimulant and non-stimulant drugs can reduce symptoms of attention deficit/hyperactivity disorder ( ADHD ) . The stimulant drug methylpheni date ( MPH ) and the non-stimulant drug atomoxetine ( ATX ) are both widely used for ADHD treatment , but their differential effects on human brain function remain unclear . We combined event-related fMRI with multivariate pattern recognition to characterize the effects of MPH and ATX in healthy volunteers performing a rewarded working memory ( WM ) task . The effects of MPH and ATX on WM were strongly dependent on their behavioral context . During non-rewarded trials , only MPH could be discriminated from placebo ( PLC ) , with MPH producing a similar activation pattern to reward . During rewarded trials both drugs produced the opposite effect to reward , that is , attenuating WM networks and enhancing task-related deactivations ( TRDs ) in regions consistent with the default mode network ( DMN ) . The drugs could be directly discriminated during the delay component of rewarded trials : MPH produced greater activity in WM networks and ATX produced greater activity in the DMN . Our data provide evidence that : ( 1 ) MPH and ATX have prominent effects during rewarded WM in task-activated and -deactivated networks ; ( 2 ) during the delay component of rewarded trials , MPH and ATX have opposing effects on activated and deactivated networks : MPH enhances TRDs more than ATX , whereas ATX attenuates WM networks more than MPH ; and ( 3 ) MPH  mimics reward during encoding . Thus , interactions between drug effects and motivational state are crucial in defining the effects of MPH and ATX This paper presents a new multigroup classification method based on subtle differences in regional brain activity during the completion of a functional magnetic resonance imaging ( fMRI ) challenge paradigm . Classification is performed based on features derived from BOLD time intensity curves in selected regions of interest ( ROI ) . For each ROI , a mean time intensity curve [ called mean regional response ( MRR ) ] is calculated from realigned and normalized data sets . The overall subject performance is characterized with a vector of features obtained using nonlinear modeling of all subject 's MRRs with a mixture of time shifted Gaussian functions . The classification is performed in the reduced-dimension optimal discrimination space , obtained through canonical transformations of original feature space . In order to demonstrate feasibility of the proposed method , classification of three groups of subjects is presented . The three groups are defined as heavy marijuana smokers after 24 hours of abstinence , heavy marijuana smokers after 28 days of abstinence , and healthy nonusing controls . The proposed method can be useful as an analytic tool for the discrimination of different groups of subjects based on temporal features of functional magnetic resonance imaging activation The aim of this work is to present an automated method that assists in the diagnosis of Alzheimer 's disease and also supports the monitoring of the progression of the disease . The method is based on features extracted from the data acquired during an fMRI experiment . It consists of six stages : ( a ) preprocessing of fMRI data , ( b ) modeling of fMRI voxel time series using a Generalized Linear Model , ( c ) feature extraction from the fMRI data , ( d ) feature selection , ( e ) classification using classical and improved variations of the R and om Forests algorithm and Support Vector Machines , and ( f ) conversion of the trees , of the R and om Forest , to rules which have physical meaning . The method is evaluated using a data set of 41 subjects . The results of the proposed method indicate the validity of the method in the diagnosis ( accuracy 94 % ) and monitoring of the Alzheimer 's disease ( accuracy 97 % and 99 % ) 
    Output:
    
There is some evidence that , in particular , support vector machines seem to be promising .