working memory deficits are considered to be a central manifestation of the pathophysiology of schizophrenia ( forbes et al . , 2009 ) and behavioural deficits in working memory processing ( pflueger et al . , 2007 )
are already evident before the onset of the disorder in individuals with an at - risk mental state ( arms ) ( fusar - poli et al . , 2012d ) .
alterations in functional brain activity ( smieskova et al . , 2012a ) during working memory processing have been reported in arms subjects . in comparison to healthy controls ( hcs ) , subjects at high risk for psychosis
exhibited reduced prefrontal and parietal activation during the n - back task ( fusar - poli et al . , 2010 ) .
there is increasing evidence that vulnerability to psychosis is associated with dysfunctional connectivity ( schmidt et al . , 2013b ) .
( 2009 ) demonstrated a progressive increase in dysfunctional frontotemporal connectivity during a working memory task from hc to arms individuals and further to patients with fep . beyond connectivity
, arms is also associated with abnormalities of regional brain structure ( fusar - poli et al .
2010 ) , activity ( broome et al . , 2010 ) , and neurochemistry ( allen et al . , 2012 ; fusar - poli et al . , 2011b ) that are qualitatively similar to but less severe than those in patients with overt psychosis ( fusar - poli et al . ,
there is also evidence suggesting that within arms individuals , prefrontal dysfunction during wm is related to grey matter abnormalities in the same regions ( fusar - poli et al .
however , it is still unclear whether functional brain activity during working memory processing can be used for the individual classification and prognosis of patients at high clinical risk . at present , individuals considered at high - risk for psychosis are clinically identified according to the pace ( personal assessment and crisis evaluation clinic , melbourne ) criteria if they present with  attenuated  psychotic symptoms , full - blown psychotic symptoms that are brief and self - limiting ( riecher - rossler et al .
; yung et al . , 2004 ) , or a significant decrease in functioning in the context of a family history of schizophrenia ( fusar - poli et al . , 2012b ) .
this can lead to the correct prediction of a subsequent transition to a first psychotic episode in only 29% ( fusar - poli et al . , 2012a ) of arms individuals after 2  years and in 35 ( nelson et al .
thus , in practical terms , it is difficult to predict which subjects with an arms will later develop psychosis on the basis of their presentation of clinical features and neuropsychological markers ( fusar - poli et al .
multivariate automatic pattern classification of individuals at high - risk may be a promising approach to predicting the development of psychoses in individuals with arms ( lao et al .
these methods categorise individuals by identification of the multivariate statistical properties of the data that discriminate between groups of subjects ( klppel et al . , 2008b ; lao et al . , 2004 )
in this context , support vector machines ( svms ) have emerged as a powerful tool , as these machines can learn to categorise complex , high dimensional training data and to generalise the learned classification rules to new data ( koutsouleris et al . , 2012 ; noble , 2006 ) .
svms use information from all available voxels , which are combined to reflect differences between groups ( klppel et al .
, 2008a ) , in order to create classifiers that allow the clinician to make predictions for newly acquired ( or unseen ) data ( klppel et al . , 2008b ; rizk - jackson et al . , 2011
svms have been successfully applied to structural mri data and can distinguish between arms subjects and healthy controls ( hcs ) with high accuracy ( borgwardt et al .
, 2013b ; koutsouleris et al . , 2012 ; koutsouleris et al . , 2009a )
a limited number of studies have employed mri data to investigate neurofunctional classifiers in individuals at risk of psychosis , in order to demonstrate that subtle differential functional patterns subserving emotional processing may make a major contribution towards identifying individuals who tend towards psychosis ( modinos et al . , 2012 ; modinos et al . , 2013 ) .
despite substantial evidence of working memory deficits both , at the time of first episode of the disease ( mesholam - gately et al . , 2009 ) , and predating the onset of psychosis ( fusar - poli et al . , 2012c )
, so far it has not been assessed if discriminative information regarding vulnerability for psychosis resides in working memory alterations . in this study
, we sought to examine whether subjects with an arms can be identified on the basis of their individual response within a working memory network of regions activated in a verbal identity - monitoring variant of the n - back task ( owen et al . , 2005 ) .
we used previously collected contrast images of fmri data ( smieskova et al . , 2012a ) and applied pattern classification using linear svms and leave - one - out cross - validation ( klppel et al . 2008b , klppel et al . 2009 ) .
2013 ) svm mri studies of subjects with an arms , we hypothesised that prefrontal activations could make a predominant contribution to the classification of the arms . on the assumption that increasing task demand increases the magnitude of neurofunctional abnormalities in arms ( fusar - poli et al . , 2007b ) , we expected robust discrimination of arms and hc , with high classification accuracies .
on the other hand , we expected that it would be much more difficult to differentiate arms and fep patients on the basis of their working memory activations ( borgwardt et al . , 2013a ) .
subjects with an arms and fep patients were assessed at the time of mri scan .
inclusion required one or more of the following : ( a )  attenuated  psychotic symptoms , ( b ) brief limited intermittent psychotic symptoms ( blips ) , or ( c ) a first degree relative with a psychotic disorder plus at least two indicators of a clinical change , such as a marked decline in social or occupational functioning .
basel screening instrument for psychosis ( bsip ) ( riecher - rossler et al . , 2007b ) , the brief psychiatric rating scale ( bprs ) ( lukoff et al . , 1986 ) , the scale for the assessment of negative symptoms ( sans ) ( andreasen , 1989 ) , and the global assessment of functioning ( gaf ) .
symptoms occurring in the previous 5  years ; nonspecific  prodromal  signs ( riecher - rossler et al . , 2007b ) occurring in the previous 2  years ; previous or current psychotic symptoms , psychosocial functioning over the last 5  years , substance dependency ; and psychotic disorders in first and second degree relatives ( riecher - rossler et al . , 2008 ) .
the group of individuals with an arms corresponds to the personal assessment and crisis evaluation ( pace ) criteria by yung et al .
current and previous psychotropic medication , alcohol , nicotine , cannabis , and consumption of other illegal drugs were investigated by using a semi - structured interview adapted from the early psychosis prevention and intervention centre ( eppic ) drug and alcohol assessment schedule ( http://www.eppic.org.au ) .
the exclusion criteria for these subjects were : history of previous psychotic disorder treated with antipsychotics ; psychotic symptomatology secondary to an  organic  disorder ; substance abuse according to the icd-10 research criteria ; psychotic symptomatology associated with an affective psychosis or a borderline personality disorder ; age under 18  years ; insufficient knowledge of the german language ; and iq less than 70 ( lehrl et al . , 1995 ) . in arms subjects ,
clinical follow - up occurred on average 4.8    8.6  months ( range  =  024  months ) after their baseline mri scans . during the follow - up period , 4 arms subjects ( 21% ) made a transition to a first episode of psychosis , according to the pace criteria ( yung et al . , 1998 )
all subjects were representative of the local population of individuals presenting with an arms or fep in terms of age , sex , handedness , and alcohol and cannabis consumption .
these individuals had no current psychiatric disorder , no history of psychiatric illness , head trauma , neurological illness , serious medical or surgical illness , substance abuse , and no family history of any psychiatric disorder as assessed by an experienced psychiatrist in a detailed clinical semi - structured interview .
all participants provided written informed consent , and the study was approved by the research ethics committee .
functional data were acquired on a 3  t scanner ( siemens magnetom verio , siemens healthcare , erlangen , germany ) using an echo planar sequence with a repetition time of 2.5  s , echo time of 28  ms , matrix 76    76 , 126 volumes and 38 slices with 0.5  mm interslice gap , that gave a resolution of 3    3    3  mm , and a field of view of 228    228  cm . a well - established n - back working memory task ( smieskova et al . ,
2012a ) was administered to patients and controls . with an inter - stimulus interval of 2  s ,
all subjects were presented with a series of black letters on a white background in a prismatic mirror .
each stimulus was presented for 1  s. the size of the letters was 8  cm projected onto the screen at the end of the scanner . during the baseline ( 0-back ) condition , subjects were required to press the button with the right hand when the letter  x  appeared . during 1-back and 2-back conditions ,
participants were instructed to press the button if the currently presented letter was the same as that presented 1 ( 1-back condition ) or 2 ( 2-back condition ) trials previously .
30  s blocks ( 2    1-back , 3    2-back , and 5    0-back ) , matched for the number of target letters per block ( i.e. 2 or 3 ) , in a pseudo - random order .
the reaction times and response accuracy were recorded on - line . we pre - processed and analysed the mr images using the statistical parametric mapping software ( spm8 ; wellcome department of cognitive neurology , london , united kingdom ) .
all mri volumes were realigned to the first volume , corrected for motion artefacts , mean adjusted by proportional scaling , with affine and non - linear registration , normalised into standard space ( mni template ) , and smoothed using an 8  mm fwhm gaussian kernel ( modinos et al . , 2013 ) .
maximum likelihood parameter estimates were calculated at the first level at each voxel using the general linear model .
our design matrix included an autoregressive ar(1 ) model of serial correlations and a high - pass filter with a cutoff of 128  s. we convolved the onset times for each block in seconds with a canonical haemodynamic response function .
the first level design matrix included four conditions ( with onset times of 0-back , 1-back , 2-back , and errors in seconds ) in a block design and a multiple regressor for motion corrections .
each task condition ( 1-back and 2-back ) was then contrasted against the baseline condition ( 0-back ) in each subject . for svm analysis we used 2-back more than 0-back contrasts known to comprise information about attention - independent modality with a higher load level of working memory . for the fmri
additionally , behavioural performances during the 2-back working memory condition were previously analysed by schmidt et al .
the sensitivity index was calculated using the formula z(hits )    z(false alarms ) .
the equal weighting of hits and false alarms provides an objective measure of sensitivity that is independent of participant response bias .
when the anova null hypothesis of equal means was rejected , we used bonferroni - corrected post - hoc t tests .
the pattern classification of the contrast images obtained under the 2-back  >  0-back condition was based on three different 2-class prediction models : hc versus arms , hc versus fep , and arms versus fep , using linear svm combined with a leave - one - out cross - validation strategy ( klppel et al . , 2008b ) .
an anatomical region of interest mask was created using wfu pickatlas ( theory of the talairach brain atlas ) toolbox implemented in spm8 ( maldjian et al . , 2003 ) .
the mask included the main regional loci of brain activation of verbal identity - monitoring variant of the n - back task identified by a meta - analysis : lateral premotor cortex ( ba6 ) , dorsal cingulate / medial premotor ( sma ) ( ba 32,6 ) , dorsolateral prefrontal ( ba46,9 ) , ventrolateral prefrontal ( ba44 ) , frontal pole ( ba10 ) , medial posterior parietal ( ba7 ) , inferior parietal lobule ( ba40 ) , medial and lateral cerebellum , and thalamus ( owen et al . , 2005 ) . for comparison , pattern classification without this mask was also performed .
libsvm , a library for svms ( http://www.csie.ntu.edu.tw/-cjlin/libsvm ) , running under matlab 7.1 ( mathworks , usa ) , was used for pattern classification analysis .
the svm methodology has been detailed elsewhere ( klppel et al . , 2008b ) .
in brief , svms include a training step to learn about systematic differences in the data with respect to the two groups that are to be classified . in the context of machine learning ,
individual bold images are treated as points located in a high dimensional space , where the total number of dimensions is determined by the number of voxels within the mask .
svms are trained to establish a decision rule that should be sufficiently general to allow discrimination of new ( testing ) data .
new scans can then be tested against trained sets and in turn categorised as members of a particular clinical group ( klppel et al . , 2008b ) .
a linear kernel matrix was created from the images , which can be viewed as a similarity measure among subjects belonging to a characterised group .
the images cluster in subspaces containing images that are very similar , so that image normalisation into a standard space is an important pre - requisite .
the use of an svm for image classification is an example of linear discrimination . as a binary classifier
, it divides the space of bold images into two classes separated by a hyperplane .
the optimal separating hyperplane ( forbes et al . , 2009 ) produced by an svm is defined by those images that are closest to the separating boundary between them , i.e. the images that are most ambiguous , called 
support vectors. after training , an osh contains learned differences between classes ; for example , in the present case , this may be arms and hc images .
the trained classifier can be used on scans that have not yet been presented to the svm algorithm .
this information is used in the prediction step to assign any new ( or previously retained ) image to one of the learned classes .
leave - one - out cross - validation iteratively leaves successive images out of the training data set for subsequent class assignation until each one has been used to make a prediction .
thereby , the method permits the estimation of the overall classification accuracy and the potential generalisability of a prediction model ( klppel et al .
weight vector maps were created to show the brain regions that best discriminate between groups . in order to test
how well svms can differentiate different stages of psychosis , 6 separate prediction models for different 2-group predictions were created  with or without a mask ( models i  vi ; see table 3 for listing ) ( results for contrast iv  see fig .
the statistical significance of classification accuracy was determined by permutation testing ( for a total of n  =  100 permutations ) .
this involved repeating the classification procedure with training group labels randomly allocated by the computer n times in order to generate a null distribution of accuracies .
the fraction of permutations achieving a higher accuracy than the true labels provides an estimate of the significance of the obtained accuracy relative to chance .
the slack parameter c was optimised for each instance during leave - one - out cross - validation by performing a parameter search , where the highest classification accuracy determined the optimal value of c. population variables are expressed as means and standard deviations ( ordinal scales ) or as totals and percentages ( nominal scales ) .
group comparisons were performed via analysis of variance ( anova ) for parametric data and via chi - square tests for non - parametric data .
the tukey - hsd correction was calculated for anova post - hoc analysis . a statistical level of p  <  0.05 was considered significant .
groups were well - matched for age ( f(2,54 )  =  0.549 , p  =  0.581 ) , sex ( (2 )  =  2.927 , p  =  0.231 ) , and handedness ( (2 )  =  0.538 , p  =  0.764 ) .
they differed in positive symptoms ( sum of suspiciousness , hallucinations , delusions , and conceptual disorganisation scores of bprs ; f(2,40 )  =  27.587 , p  <  0.0001 ) , negative symptoms ( total score of sans , f(2,52 )  =  22.226 , p  <  0.0001 ) , and global functioning ( total score of gaf ; f(2,54 )  =  38.237 , p  <  0.0001 ) .
the fep and arms groups had more positive ( p  =  0.007 and p  <  0.001 ) and negative symptoms ( p  =  0.0001 ) , and worse global functioning ( p  =  0.001 ) than those of hc , as calculated in anova with tukey 's honest significance difference tests .
the fep and arms groups did not differ regarding antidepressant medication ( 37% in both groups ) at the mri date .
groups did not differ with respect to alcohol , cannabis and cigarette consumption at mri .
( at p  =  0.05 ) was calculated for post - hoc analysis in spss 19.0 .
table 2 shows the regions most relevant to discriminating between arms and hc ( as given by svm weights ) . if the analysis was constrained to the verbal n - back network ( mask ) , of the three prediction models , the highest accuracy ( se 89.5% , sp 63.2% , ta 76.2% , p 
=  0.01 ) was found for arms vs. hc ( model i ) , only 2 arms individuals of 19 were wrongly classified as hc .
low accuracies were observed in models ii ( se 52.6% , sp 47.4% , ta 50.0% , p  =  0.46 ) and iii ( se 42.1% , sp 52.6% , ta 47.4% , p  =  0.62 ) ; here , half of the individuals were wrongly classified . without analysis mask ,
classification accuracies were equal for model iv ( se 73.7% , sp 57.9% , ta 65.8% , p  =  0.03 ) and model v ( se 73.7% , sp 57.9% , ta 65.8% , p  =  0.0047 ) , and low for model vi ( se 52.6% , sp 63.2% , ta 57.9% , p  =  0.18 ) ( table 3 ) . fig .
1 shows the weight vector map with the most discriminating brain regions between the groups ( top 5% ) .
the threshold of classification strength was set to show the top 5% of the weight vector scores , as done in previous studies addressing other aspects of pattern classification and psychosis proneness ( modinos et al . , 2013 ; modinos et al . , 2012 ) .
2 shows the classification scores , i.e. the projection of subjects onto the weight vector with positive patterns ( blue circles ) discriminating for group i , and negative patterns ( red crosses ) for group ii for hc versus arms ( ( a ) no mask , ( b ) verbal n - back mask ) , hc versus fe ( ( c ) no mask , ( d ) verbal n - back mask ) , and arms versus fe ( ( e ) no mask , ( f ) verbal n - back ) . models ( group i vs. group ii ) : hc  arms ( a ) no mask and ( b ) verbal n - back mask ; hc  fe ( c ) no mask and ( d ) verbal n - back mask and arms  fe ( e ) no mask and ( f ) verbal n - back .
the present study combines task - induced fmri data and machine learning to explore whether functional brain activity during n - back working memory processing demonstrates a functional correlation with susceptibility for psychosis . using svm
we demonstrate that people at high - risk for psychosis can be correctly classified with accuracies of up to 7490% .
the best performance was achieved when the analysis was restricted to brain regions within the verbal working memory network ( owen et al . , 2005 ) .
a large number of fmri studies focusing on the neural basis of verbal working memory show a remarkably consistent network of neural areas active when verbal working memory tasks are carried out ( for review see owen et al . , 2005 ) .
the main components making up the working memory network within the lateral prefrontal cortex are the dorsal lateral prefrontal cortex ( ba9 , 10 and 46 , mostly bilateral ) , the ventral lateral prefrontal cortex ( ba44 ; brodmann 's area ) and ba45 ) and the premotor cortex ( ba6 , mostly bilateral ) . in the frontal lobe there are also secondary motor areas ( ba6 ) , which include parts of the anterior cingulate cortex ( acc ) when activated .
bilateral activations in the posterior parietal lobes are found in the dorsal areas of the supra - marginal gyrus ( ba40 ) and in the cerebellum .
the exact activation patterns vary , depending on the characteristics of the given verbal working memory task . restricting the svm analysis to these regions , we accurately identified differential activation patterns between arms subjects and hc ( cf .
this is in line with our hypothesis that a network of working memory activations , within the lateral prefrontal cortex contributes predominantly to the identification of high - risk subjects .
the low accuracies found by classifying arms versus fep ( table 3 , models iii and vi ) support our second hypothesis that increasing task demand increases the magnitude of neurofunctional abnormalities in arms and makes it nearly impossible to distinguish arms from fep using svm ( borgwardt et al .
lawrie et al . ( 2002 ) predicted that individuals with schizophrenia would exhibit decreased connectivity between dorsal lateral prefrontal cortex ( brodmann 's areas 9 and 10 ) and superior temporal gyrus ( stg ) which is involved in auditory processing .
most of the fmri studies reviewed in a meta - analysis of the neurofunctional correlates of vulnerability to psychosis ( fusar - poli et al . , 2007b )
reported reduced dorsal lateral prefrontal cortex activation in fep subjects during cognitive tasks , which suggests that hypofrontality in the dorsolateral prefrontal cortex may be a feature of fep .
three studies of working memory in fep reported decreasing activation in the dorsolateral prefrontal cortex during the hard level of the task ( mendrek et al . , 2004 ; rasser et al .
2001 ) , and only one study reported greater prefrontal activation in fep subjects ( fusar - poli et al . , 2007b ) .
in contrast , a number of genetic high - risk studies reported relatively greater prefrontal activation in high - risk subjects than in controls ( callicott et al . , 2003 ; seidman et al .
2004 ) , which most probably reflects a compensatory response to maintain adequate performance ( fusar - poli et al . ,
notably , in the present study , fep patients could not be distinguished from hcs .
previous fmri studies have shown that antipsychotic treatment can modulate brain activity during cognitive tasks ( fusar - poli et al .
, 2007a ; snitz et al . , 2005 ) and , more specifically , functional connectivity in circuits that are involved in mediating the cognitive symptoms in schizophrenia ( stephan et al . , 2001 ) . in fep patients after 6  weeks of treatment with second - generation antipsychotic drugs , frontoparietal
temporal network connectivity during resting state was no longer significantly different from controls ( lui et al . , 2010 ) .
antipsychotic treatment also normalises frontoparietal connectivity during a working memory task in fep patients ( schmidt et al .
, 2013a ) , indicating an improved cognitive performance after antipsychotic medication ( see also the limitations section ) .
this is consistent with studies in early - psychosis patients ( keefe et al . , 2007a ) and in those with chronic schizophrenia ( keefe et al . ,
over the past years , the prodromal phase of psychosis has been intensively studied using mri .
potential mri - based biomarkers of vulnerability to psychosis have been described in only a few studies ( borgwardt et al .
, 2012 ; koutsouleris et al . , 2009a ; modinos et al . , 2012 ) , despite increasing evidence supporting structural and functional brain alterations prior to the onset of schizophrenia ( schmidt et al .
deficits in neurocognitive tasks indexing working memory in high - risk subjects were shown in a quantitative meta - analysis ( fusar - poli et al . , 2012b ) .
those subjects at high - risk who later developed psychosis , showed poorer performance in working memory than those who did not develop the illness .
thus , impairments in working memory specifically predicted the later transition to psychosis ( fusar - poli et al . , 2012b ) .
our previously published conventional fmri analysis showed that functional working memory abnormalities may occur prior to the transition to psychosis ( smieskova et al . , 2012a ) .
significant activations were found in the bilateral spl and mfg during the n - back task in all groups ( smieskova et al . , 2012a ) , consistent with previous n - back studies in arms individuals ( broome et al . , 2010 ; fusar - poli et al . , 2011a ) .
( 2013b ) showed that effective connectivity between frontal and parietal regions modulated by a high - load wm condition was gradually reduced from hcs to individuals with an arms for psychosis and further to non - treated fep patients ( schmidt and borgwardt , 2013 ; schmidt et al .
the main potential of svm - based classification is that it might be useful for predicting the clinical transition to psychosis at the individual level .
most recently , automatic pattern classification methods have been considered to promote a potentially accessible and objective way to improve clinical decision making ( mandl et al . , 2013b ) , and may present a measure of the risk of developing psychosis in individuals with an arms if sufficiently accurate ( borgwardt et al . , 2013b ) .
recent efforts have investigated the diagnostic value of neuroimaging biomarkers in the very earliest stages of the illness .
( 2009 ) used a cortical pattern matching method to compare hcs to patients with recent - onset psychosis .
patients showed lower grey matter density , particularly in the prefrontal , cingulate and lateral temporal brain regions relative to the controls , and the pattern classification analysis using a leave - one - out cross - validation was able to discriminate between the two groups with an accuracy of 86.1% .
consistent with this finding , structural mri studies discriminated between hcs and those with an arms based on grey matter volume with 82% accuracy ( borgwardt et al .
, 2013b ; koutsouleris et al . , 2012 ; koutsouleris et al . , 2009b
furthermore , pattern classification based on brain activation during emotional processing classified subjects with susceptibility to psychosis with accuracies up to 69.4% ( modinos et al . , 2012 ) .
focusing on changes along the psychosis timeline , svms based on structural grey matter patterns have discriminated between individuals who did , and did not , transition from the arms to full blown psychosis with accuracies of up to 92% ( borgwardt et al .
mourao - miranda et al . ( 2012 ) used an svm whole - brain classification approach to predict future illness course at the individual level from mri data obtained at the first psychotic episode .
continuous psychosis from  episodic psychosis and  continuous psychosis from hcs with accuracies of 70% and 67% , respectively , but they were unable to distinguish 
( 2015 ) showed that cortical surface - based pattern classification predicted good vs. poor outcome status at follow - up with an accuracy of 82% as determined by nested leave - one - cross - validation .
neuroanatomical prediction involved cortical area reductions in the superior temporal , inferior frontal and inferior parietal areas and was not confounded by functional impairment at baseline , or antipsychotic medication and transition status over the follow - up period .
the classification accuracies based on these structural studies suggest , that longitudinal measures may in the future also further improve classification based on svm analysis of fmri data .
firstly , the rather small sample sizes of the present study may have limited the hc versus psychosis classification , and thus the findings may have been influenced by the heterogeneity of the psychosis subgroup . to account for this , we controlled for the potential effects of covariates , such as age , gender , and education . secondly , to ensure that the data used for testing does not overlap with those used for training two completely different sets of data would be best practice .
therefore , in order to obtain a relatively unbiased estimate of generalisability ( hastie et al . , 2001 ) , here we used leave one cross - validation , in which the total sample is split into training and testing data several times using different partitions and the resulting accuracies of classification are averaged across repetitions ( lemm et al . , 2011 )
. thirdly , although we performed pattern classification in matched groups with possibly low antipsychotic rates , we can not fully exclude the confounding effects of antipsychotic medications ( ettinger et al .
individuals who performed the task badly had been excluded from the fmri n - back analysis ( selnes et al .
furthermore , instead of evaluating the number of omissions or error rates , schmidt et al . ( 2013a ) analysed the 2-back working memory performances using signal - detection theory .
the sensitivity index provides an objective measure independent of the individual 's response bias . when equally weighting hit rates and false alarms , the 2-back working memory performance did significantly distinguish non - treated fep patients from hcs , whereas the working memory performance in arms individuals came out in the middle of both groups .
moreover , no significant difference was found in antipsychotic - treated fep individuals compared with hcs , indicating an improved cognitive performance after antispychotic medication ( yao et al . , 2013 ) .
fourthly , other factors such as cannabis use and smoking might have played a confounding role .
however , the latter seems to be rather unlikely , as we did not find significant differences between groups ( table 1 ) .
however , given the low number of n  =  4 individuals with psychosis resulting from arms after 33.3  months of clinical follow - up , this was not possible in the present study .
transition analysis may in the future further improve classification accuracy . generally , it is unclear whether supervised mri - based pattern recognition can achieve the level of sensitivity and specificity needed in order to be integrated into clinical applications . in the present study ,
only 63% of controls were correctly classified , which limits the diagnostic applicability of the model to the healthy population .
in the future , feature selection methods and the use of an independent test data set may further increase classification accuracy . however , a reduced number of voxels would also limit the interpretation of the results , in particular if different regions are compared .
applied linear svm to n - back fmri data ( 2  >  0 back ) in 41 patients with chronic schizophrenia and 42 hcs ( brodersen et al . , 2014 ) .
parameter estimates from a dynamic causal model ( dcm ) of a visual  parietal  prefrontal network were used to define a model - based feature space for the subsequent application of supervised and unsupervised ( gaussian mixture modelling ) learning techniques .
accuracy was 55% when local activity was used , but rose to 62% when functional connectivity was used and was even better ( 78% ) with dcm - based effective connectivity estimates .
with dcm parameters , an unsupervised approach showed nearly the same accuracy ( 71% ) . automatic pattern classification based on functional working memory data
might contribute to monitor disease development in people at clinical high - risk for psychosis and improve clinical decisions in clinically diagnosed arms .
furthermore , the shift from single predictive models to ensembles of classifiers may produce more generalisable diagnostic biomarkers by averaging the diagnostic decisions of numerous predictive models ( koutsouleris et al . , 2010 ) . because it is difficult to predict which high - risk individuals will go on to develop psychosis ( fusar - poli et al .
, 2012a ) there is a clear clinical need for other markers that could be used to help clinicians identify the subgroup of subjects that will benefit most from preventive interventions ( fusar - poli et al . , 2013 ) .
our results reveal that svm analysis of working memory - related fmri data can identify arms subjects with high accuracy when the analysis is restricted to a distributed and subtle set of brain regions within the verbal working memory network .
this might have implications for clinical decision making concerning the early detection of individuals at high - risk of developing psychosis .
thus , in addition to the abnormalities in neuroanatomical patterns ( koutsouleris et al . , 2009a ) , including measures of neuro - functional activity may improve the classification accuracy for people at high - risk for psychosis .
 the psychosis high - risk state is accompanied by alterations in functional brain activity during working memory processing . 
 we used binary automatic pattern - classification to discriminate between the at - risk mental state ( arms ) , first episode psychosis ( fep ) and healthy controls ( hcs ) based on n - back wm - induced brain activity . 
 linear support vector machines and leave - one - out - cross - validation were applied to fmri data of matched arms , fep and hc ( 19 subjects / group).the hc and arms were correctly classified , with an accuracy of 76.2% ( sensitivity 89.5% , specificity 63.2% , p  =  0.01 ) using a verbal working memory network mask . 
 only 50% and 47.4% of individuals were classified correctly for hc vs. fep ( p  =  0.46 ) or arms vs. fep ( p  =  0.62 ) , respectively . without mask , accuracy was 65.8% for hc vs. arms ( p  =  0.03 ) and 65.8% for hc vs. fep ( p  =  0.0047 ) , and 57.9% for arms vs. fep ( p  =  0.18 ) . 
 regions in the medial frontal , paracingulate , cingulate , inferior frontal and superior frontal gyri , inferior and superior parietal lobules , and precuneus were particularly important for group separation.these results suggest that fep and hc or fep and arms can not be accurately separated in small samples under these conditions . 
 however , arms can be identified with very high sensitivity in comparison to hc . 
 this might aid classification and help to predict transition in the arms . 
Introduction
Aims of the study
Methods
Results
Discussion