attention - deficit and hyperactivity - disorder ( adhd ) affects 58% of the worldwide childhood population , it has high comorbidity and it shares symptoms with other behavioral and emotional disorders ( boyle et al . , 2011 ; larson et al . , 2011 ) .
adhd prevalence rates vary significantly between and within countries , and depend on the ascertainment method and criteria utilized . of particular concern
is the marked variability in diagnostic rates of adhd in developed countries ( getahun et al . , 2013 ) .
this may reflect increased awareness of teachers and parents to symptoms of adhd in communities with a focus on education and adequate health care .
however , even within communities there is marked variability in rates of adhd due to the subjective nature of primarily descriptive ( symptom - based ) diagnostic procedures ( asherson et al . , 2012 ; polanczyk et al . , 2014 ; see also recent reports by the us center for disease control and prevention ) .
such diagnostic heterogeneity and its poor reliability have hampered efforts to determine the pathophysiology of adhd ( morgan et al . , 2013 ) .
attempts have been made to find neuroimaging - based biomarkers of adhd using structural magnetic resonance imaging ( johnston et al .
2013 ) , resting - state functional mri ( hoekzema et al . , 2014 ; tomasi and volkow , 2014 ) , fmri data acquired during a single cognitive task ( hale et al . , 2015 ; hart et al .
, 2014a ; hart et al . , 2014b ) , or combinations of some of these techniques ( anderson et al . , 2014 ;
these studies provide mixed insights regarding the possible underlying neurocognitive deficit of adhd , and they report moderate classification accuracies ( rarely exceeding 80% ) , insufficient for clinical diagnosis .
adhd is characterized by behavioral symptoms and multiple cognitive deficits associated with context dependent abnormal patterns of neural activity distributed across multiple brain regions .
consequently , adhd is unlikely to be characterized by localized structural brain abnormalities robust enough to be detected using available structural imaging techniques .
it is also unlikely that significant functional brain abnormalities characterizing adhd would be evident regardless of the mental state of the test subject , during the scan ( castellanos and proal , 2012 ; hammer et al . , 2015 ) .
key characteristics of adhd include poor working memory , greater reliance on external feedback , and abnormal reward processing .
these are associated with altered patterns of activity in distinct brain networks : ( i ) attention and working memory , comprising the dorsolateral prefrontal cortex , parietal cortex and temporal cortices ( burgess et al . , 2010 ; ehlis et al . , 2008 ;
vance et al . , 2007 ) ; ( ii ) executive functions and cognitive control ( including feedback processing and response selection ) , comprising the dorsal , medial and ventral frontal cortices ( booth et al . , 2005 ; clark et al .
, 2007 ; sonuga - barke and fairchild , 2012 ) ; and ( iii ) reward - processing , comprising the orbitofrontal cortex , anterior cingulate cortex and basal ganglia ( del campo et al . , 2013 ; plichta et al .
notably , there are substantial individual differences in reactions to reward and feedback manipulation in adhd ( demurie et al . , 2011 ;
, 2015 ; hammer et al . , 2015 ; plichta and scheres , 2014 ; van der schaaf et al . ,
accordingly , we hypothesized that using fmri data from an ensemble of visuospatial working memory ( vswm ) tasks that differ in the motivational context ( determined by the availability of reward and feedback ) would increase the odds that abnormal patterns of brain activity would be reliably detected in a larger proportion of adhd cases .
we expected this to enable more accurate detection of adhd cases than the accuracies obtained by using fmri data acquired from any single vswm task , or the accuracy based on the participant 's respective behavioral performance .
given the central role of vswm in adhd , we compared fmri data from four distinct vswm tasks in boys with adhd and typically developed ( td ) boys .
tasks differed in the availability of trial - by - trial feedback ( feedback versus no - feedback ) and the participant 's expectation for significant monetary reward ( large versus small ) .
all tasks required tracking the spatial location of letters while ignoring the letters ' identity , and executing timely responses .
the manipulation of feedback and reward provided different motivational contexts , each requiring somewhat distinct executive skills found to be impaired in children with adhd .
we used a multimodal analysis based on relatively few brain regions of interest , discovered using an independent univariate analysis ( hammer et al .
, 2015 ; morris et al . , 2012 ; mulligan et al . , 2011 ; thoma and henson , 2011 ) . using a sparse principal component analysis
, we further reduced the number of variables that were provided as input to the classifier .
this substantially limited the odds of discovering an overfitted adhd classification model , and simplified the interpretation of the discovered model .
we used a logistic regression ( lr ) classifier , which directly models the class conditional probabilities for each case ( i.e. , calculating the predicted probability that a given child has adhd ) by attempting to find a model allowing a decisive classification of as many cases as possible .
this attribute is important in clinical settings where we aim to find a model that allows classification with confidence ( nouretdinov et al . , 2011 ) .
twenty boys with a diagnosis of adhd combined - type ( mean age in years  =  10.42    sd  =  0.80 ) and 20 typically developed ( td ) boys ( 10.96    0.91 ) participated in the experiment . participants gave their informed consent ( and parental consent ) in accordance with the policies of the institutional review board ( irb ) at northwestern university .
at the time of the fmri scanning session , adhd youth were weaned off stimulant medication for at least 24  h ( 12 participants used prescribed stimulants on a regular basis during the time they participated in this study ) .
adhd diagnoses were based on exceeding the clinical cut offs on the adhd rating scale ( dupaul et al . , 1998 ) and the semi - structured diagnostic interview , the kiddie schedule for affective disorders and schizophrenia for school aged children : present and lifetime ( k - sads - pl ) version ( kaufman et al . , 1997 ) .
mean total adhd score was higher in the adhd group than in the td group ( supplemental table  1 ) .
participants were excluded if they had been diagnosed with a neurological disorder or were treated medically for a comorbid psychiatric disorder .
all participants were right - handed native english speakers with normal or corrected to normal vision .
mean full - scale iq scores were within normal range in both groups , however the average iq in the adhd group was lower than the average iq in the td group ( supplemental table  1 ) , which is not uncommon in adhd studies ( e.g. , hart et al . , 2014a ) .
each task was 48 trials long . before and after each 2-back task the participant performed a fixation task where he was asked to press a key whenever the fixation - cross changed its color ( which happened in 4/12 of the trials ) .
e - prime  2.0 ( psychology software tools , inc . ) was used for stimuli presentation and for recording participants ' responses ( for more details about the experimental design , see hammer et al . , 2015 ) .
the two independent factors in the study were reward size ( large - reward versus small - reward ) and presence of trial - by - trial feedback ( no - feedback versus feedback ) . in an earlier practice session , and at the beginning of the scanning session ,
each participant was instructed that the reward for each correct decision in the large - reward task was 10 times larger than in the small - reward task . in the trial - by - trial feedback task , each key - press was followed by either a green square ( indicating a correct decision ) or a red square ( indicating an incorrect decision ) presented in the center of the screen . in the no - feedback task ,
the participant was informed about his overall performance only after concluding the task ( fig .  1 ) .
imaging data were acquired on a 3.0  tesla siemens tim trio scanner using a 12-channel head coil .
a susceptibility weighted single - shot epi ( echo planar imaging ) method with bold ( blood oxygenation level - dependent ) was used for functional image acquisition with the following scan parameters : tr  =  2000  ms , te  =  20  ms , flip angle  =  80 , matrix size  =  128    120 , field of view  =  220    206.3  mm , slice thickness  =  3  mm ( 0.48  mm gap ) , and number of slices  =  32 ( an effective functional voxel size of 2    2    4  mm ) . a total of 145 images ( trs )
a high resolution , t1 weighted 3d image was also acquired with the following parameters : tr  =  2300  ms , te  =  3.36  ms , flip angle  =  9 matrix size  =  256    256 , field of view  =  256  mm , slice thickness  =  1  mm , and number of slices  =  160 . the acquisition of the anatomical scan took approximately 9  min . prior to the scanning session children
this enabled confirming that the participant is capable of keeping his head still for the duration of the scanning session . to minimize head movements in the scanner , gaps between the participant 's head and the head - coil
data analysis was performed using mathworks matlab , spm8 ( statistical parametric mapping , wellcome trust centre for neuroimaging , london , uk ) , and ibm spss .
preprocessing involved : ( i ) slice timing ; ( ii ) realignment of all functional images to the 24th image .
( iii ) co - registration of the functional and anatomical images ; ( iv ) normalization of the t1 image to the mni305 template image , which is most commonly used also for analyzing fmri data of pediatric populations ( e.g. , burgund et al .
( v ) 4    4    8 mm full width half maximum ( fwhm ) gaussian kernel smoothing . ( vi ) we confirmed that movement was kept below 4  mm ( in any of the x , y , or z dimensions ) within a scan using the artrepair software .
images ( up to 9 per scan ) were realigned in artrepair , using interpolated values from the two adjacent non - outlier images .
for subsequent general linear model ( glm ) analyses , the excluded noisy images were deweighted . as reported in supplemental table  2
, the two groups did not differ in patterns of head movements , and the replacement of outlier images primarily enabled reducing the signal to noise ratio in the fmri data of both groups . in order to further reduce within scan variability in neural activity , only trials in which the participant responded correctly ( hit and correct rejection )
were modeled , with onset time - locked to the beginning of each trial ( calhoun et al .
( vii ) a high pass filter with a cut - off of 256  s was applied .
structural and functional brain images were inspected and found not to have significant image artifacts .
we confirmed that for each participant maximal head displacement ( the distance between the two most distant fmri images within a scan ) in all translational axes was no larger than the size of a voxel ( i.e. , 4  mm ) .
there were no significant between - groups differences in any head translation or rotation axes , all p  >  0.25 ( supplemental table  2 ) .
we also confirmed that it is unlikely that head movements underlie the adhd classification results we got based on the brain activity data ( supplemental fig .  1 ) .
feature detection was based on a univariate glm analysis , intended to identify functional brain regions of interest ( frois ) that showed significant activation or deactivation in all four vswm tasks , contrasted with all the fixation tasks , using the brain activity data of the adhd boys and td boys combined ( total of 40 participants ; see chu et al . , 2012 ; friston , 2012 for discussions regarding optimal sample size ) .
we found eight frois showing significant activation ( vswm  >  fixation ) and eight frois showing significant deactivation ( fixation  >  vswm ) .
we used a voxel threshold of p  <  0.01 ( family wise error [ fwe ] corrected ) , and voxel cluster threshold of p  <  0.01 ( minimum cluster size of 50 voxels ; fwe ) , where each cluster had a single significant peak at p  <  0.05 ( fwe ) .
an anatomical gray - matter mask ( using the talairach daemon brain atlas gray - matter mask , with dilate  =  3 ) , and a sphere mask with a radius of 15  mm from each froi peak voxel , constrained the froi volume .
these 16 rois likely reflect the vswm network in a broad childhood population , in varying contexts ( fig . 
for each participant we calculated the difference in mean beta values between each of the four vswm 2-back tasks and the mean of all fixation tasks in each of the 16 frois ( 2-back  fixation ; fixation2-back ) .
overall , given four vswm tasks and 16 frois , the initial number of features , characterizing each participant , was 64 ( see supplemental fig .  2 for activation profiles ) .
given the relatively large initial number of features , and the high correlations between some of the features ( see supplemental fig .  3 for the correlation matrix ) , we reduced the dimensionality of the data by using a sparse principal component analysis ( spca ) .
this resulted with a relatively small number of orthogonal principal components ( pcs ) , which together explained most of the variability in the data , recalculated based only on the few features with the highest loadings ( berthet and rigollet , 2013 ; ritchie et al .
this procedure has three major advantages : ( i ) spca enabled reducing the number of variables fed to the classifier to a number substantially smaller than the number of participants ; ( ii ) recalculating the pcs based only on features with the highest loadings enabled excluding lower weight features that were likely to add mostly noise ; and ( iii ) having each pc being affected by relatively few features , where each feature affects at most one pc , enabled better determining the underlying neurocognitive mechanisms represented by each pc .
we used sparse loading selection based on thresholding of the rotated loadings ( loading threshold  >  0.6 ; absolute weights  >  0.1 ) .
loading rotation was based on the varimax rotation method ( lu and zhang , 2012 ; ma , 2013 ; qi and luo , 2015 ; sjstrand et al . , 2006 ) .
the use of this threshold resulted with 39 features ( out of 64 ) affecting the first 10 pcs ( pcs with eigenvalues > 2 ) , where each feature affected a single pc ( fig . 
the first 10 pcs explained approximately 70% of the variability evident in the original 64 features .
each of the remaining pcs explained less than 3% of the variability in the data .
interestingly , we found each one of the first 10 pcs to reflect brain activity from several rois from the same vswm task , but not brain activity in a specific roi in several tasks .
the exception was pc-2 , which was based on the left and right middle frontal gyri in the two vswm tasks without feedback .
this supports our hypothesis that functional brain - imaging data from several distinct tasks is likely to add information useful for classification . for the learning of the adhd classification model
the lr directly models the class conditional probabilities for each case , attempting to decisively classify as many cases as possible . a standard 
, 2013 ; ponce - alvarez et al . , 2012 ) was used for finding which of the 10 pcs significantly contributed to classification accuracy . in each cross validation iteration the lr classifier was trained based on the fmri data of 39 participants , and then the goodness of fit of the learned model was evaluated based on the correlation between the clinician diagnosis of the left - out participant ( adhd or td ) and the lr classifier predicted adhd probability for this participant .
the predictive power from all iterations was averaged to determine which of the 10 pcs are with statistically significant predictive power , and for estimating the predictive power of the final model .
the reporting of a classification model based only on significant pcs has two advantages : ( i ) such a model is less likely to be overfitted or biased due to being based on too many predictors ( pcs ) , and thus it provides a more conservative estimate of the classification accuracies that can be achieved with the data at hand and ( ii ) it further reduces the number of brain regions by which the two groups of interest ( i.e. , adhd vs. tds ) differ , enabling a more parsimonious characterization of the differences in neurocognitive mechanisms between the two groups ( see supplemental fig .  5 for an illustration of the data processing pipeline ) .
running the lr with the leave - one - out cross validation showed a statistically significant contribution for adhd classification accuracies for only four pcs ( pc-2 , p  <  0.02 ; pc-3 , p  <  0.04 ; pc-4 , p  <  0.04 ; pc-8 , p  <  0.02 ) . excluding each one of these four pcs from the classification model
substantially impaired the classification accuracies , whereas adding any of the other six pcs did not increase the classification accuracies .
the classification accuracy of the model based on the four statistically significant pcs was 92.5% , with 95% sensitivity and 90% specificity .
importantly , most of the classification decisions had high predicted probability values ( pp  >  0.67 ) assigned to most adhd boys ( 75% ) , and low values ( pp  <  0.33 ) assigned to most td boys ( 80% ; fig . 
3 ) , indicating a model with an excellent fit ( omnibus test for model fit , (4 )  =  28.52 , p  <  0.0001 ) .
this accuracy level is not statistically different from perfect accuracy reflecting the clinician 's diagnosis ( 100% ) , p  =  0.12 ( one - tailed fisher exact test ; testing the hypothesis 
a permutation test shows that the classification accuracies of the above - described adhd classification model are unlikely to be discovered by chance . in each permutation
, labels of half the cases from each group were switched with the opposite group .
the mean accuracy of 15 distinct permutations ( 62.0% ) was significantly lower than that of the adhd model ( 92.5% ) , p  =  0.000 ( exact test ) ; t(14 )  =  14.97 , p  <  0.0001 ( one - tailed , one - sample t - test ) .
even the highest observed accuracy of a permuted - labels model was close to being significantly lower ( 15% ) than that of the adhd model , p  =  0.06 ( one - tailed fisher exact test ; testing the hypothesis  adhd model is better than the best permuted model  ) .
the mean correct decisive classification accuracies ( with pp < 0.33 or pp > 0.67 ) in the 15 permutations were 24.5% , versus 77.5% of the adhd model , p  =  0.000 ( exact test ) , t(14 )  =  13.59 , p  <  0.0001 ( one - tailed , one - sample t - test ; see also supplemental fig .  4 ) .
notably , the four significant pcs encompassed in the adhd model are based on fmri data from all four vswm tasks : pc-2 is based on the two tasks without feedback ; pc-3 is based on the small - reward with feedback task ; pc-4 is based on the large - reward with feedback task ; and pc-8 is based on the small - reward with feedback task ( fig .  2 ) .
in the following analysis , we further investigated the utility of using fmri data from multiple vswm tasks as compared with using fmri data from a single task .
here we used the threshold of eigenvalue larger than one ( keeping pcs that together explained more than 70% of the variance in the data ) .
adhd classification accuracies based on the fmri data from each single task ( fig .  4 ; table  1 ) were significantly lower than the classification accuracy of the model based on all four tasks ( fig .  3 ) .
the exception was the classification accuracy based on the fmri data from the small - reward with feedback task , which was 80% .
however , here the correct decisive classification accuracy ( with pp < 0.33 or pp > 0.67 ) based on the fmri data from the small - reward with feedback vswm task was 60% , which is significantly lower ( 17.5% ) than the four - task classification accuracy , p  <  0.05 ( one - tailed fisher exact test ) . feeding the lr classifier with the behavioral data from all four tasks resulted in an overall accuracy level of 75% ( 70% sensitivity ; 80% specificity ; omnibus test for model fit , (4 )  =  12.37 , p  <  0.01 ) .
this overall accuracy level is significantly lower ( 17.5% ) than the accuracy based on the fmri data from the four tasks , p  <  0.04 ( one - tailed fisher exact test ; testing the hypothesis  fmri model is better than the behavioral model  ) .
moreover , here only 57.5% of the classification decisions were both accurate and decisive , with high predicted probability values of being a boy with adhd ( pp  >  0.67 ) assigned to only 50% of the adhd boys , and low values ( pp  <  0.33 ) assigned to only 65% of the td boys ( fig . 
this accuracy level is significantly lower ( 20% ) than the decisive classification accuracy based on the four significant pcs ( based on four tasks ) , p  <  0.04 ( one - tailed fisher exact test ) .
performance levels in the four tasks were highly correlated , where a boy that exhibited low performance in one task likely exhibited low performance in the other tasks .
moreover , we found high correlations between the behavioral performances in the four vswm tasks and pc-2 ( fig . 
that is , the brain activity represented by pc-2 ( right and left mfg , tasks without feedback ) explains much of the observed variability in the behavioral data . on the other hand ,
the behavioral performances had low correlations with pc-3 , pc-4 and pc-8 , hence the additional diagnostic information provided by these pcs to the classification model . feeding the lr classifier with both the behavioral and fmri data ( 10 pcs ) resulted in exactly the same model as when feeding it with only the fmri data .
we show that using a logistic regression ( lr ) classifier , which received as input brain imaging data that was acquired while children perform four distinct vswm tasks , enabled an adhd classification accuracy of 92.5% ( fig .  3 ) .
classification accuracies that were based on the fmri data from all four tasks were significantly higher than those obtained using the fmri data from any single task .
we found that the brain regions in which boys with adhd exhibited altered pattern of brain activity differed from one task to the other .
the corresponding behavioral data from the four vswm tasks enabled an classification accuracy level of 75% ( fig . 
5 ) , which is significantly lower than those obtained using the fmri data from the four tasks .
participants who were misclassified based on the data from one vswm task were not necessarily misclassified when using the data from other vswm tasks ( fig . 
when the lr classifier was fed with the pcs calculated based on the four vswm tasks together , we observed a substantial improvement in the classification accuracy ( fig . 
this is consistent with the underlying theorem of ensemble - based classification ( rokach , 2010 ; klppel et al . , 2012 ) . as expected based on this theorem , classifying participants by using several measurements for each participant ( e.g. , fmri data from few distinct vswm tasks ) , where each measurement enables a better than chance classification accuracy , and where there is a substantial degree of independency between the measurements ( e.g. , distinct cognitive tasks ) , enabled higher classification accuracies compared to accuracies achieved by using the data from each single measurement .
our findings are with important theoretical contribution , providing additional support to the idea that the manifestation of neurocognitive abnormalities in adhd is context dependent ( dovis et al .
specifically , in the absence of trial - by - trial feedback , altered activity patterns characterizing adhd were most evident in the bilateral middle frontal gyri ( mfg ) , specifically the right - mfg ( see pc-2 weight in fig .  3 ; see feature weights in fig . 
the mfg is believed to play an executive role in the visuospatial working memory network , and a primary role in volitional ( top - down ) allocation of attention ( burgess et al .
we found that in vswm tasks without feedback , boys with adhd exhibited lower levels of activity in the mfg as compared with td boys .
this may indicate poor vswm and poor capacity in allocating attention to target stimuli in children with adhd , manifested when feedback is absent ( see cortese et al .
the fmri data from the small - reward with feedback vswm task also had a considerable contribution to adhd classification ( pc-8 and pc-3 ; fig .  3 ; fig . 
here , boys with adhd exhibited altered brain activity in a network ( pc-8 ) that primarily included the bilateral orbitofrontal cortex ( bi - ofc ) and the left fusiform gyrus ( left - ffg ) . the ofc has been reported to play two primary roles that have potential relevance to the current task : together with the inferior frontal gyrus , the anterior insula , the superior temporal cortex and the temporoparietal junction , the ofc is part of the ventral attention network , acting as a bottom - up saliency detection system determining subjective and context - dependent susceptibility to unexpected salient stimuli ( corbetta et al . , 2008 ;
the ofc was also found to be involved in the processing of reward - related information ( schoenbaum and roesch , 2005 ; pauli et al . , 2012 ) .
it is suggested that the ofc is involved in monitoring which recent actions were rewarded , and predicting which future actions are most likely to be rewarded ( kahnt et al . , 2010 ) .
the left - ffg was found to be involved in letter identification and reading ( mccandliss et al . , 2003 ; dehaene and cohen , 2011 ; mcnorgan et al . , 2013
here we found greater deactivation in these two brain regions in td boys , as compared to boys with adhd , primarily in the small - reward with feedback vswm task .
this may indicate that boys with adhd fail in suppressing task irrelevant information ( letter identity in a task that requires monitoring the spatial location of letters , and visual feedback indicating insignificant reward ) .
the features with significant loadings on pc-3 ( small - reward with feedback ) were the two frois in the right medial / superior prefrontal gyrus ( right - mefg- and right - mefg+ , fig . 
2b ) , the left - mfg , the right superior temporal gyrus ( right - stg ) , the right anterior insula ( right - antins ) , and the right supramarginal gyrus .
these brain regions are involved in feedback processing ( ferdinand and opitz , 2014 ) , bottom - up attention and in sensory integration ( prado et al .
the loadings on pc-3 ( and pc-8 ) , indicate that in the small - reward with feedback condition children with adhd are likely to exhibit altered suppression of irrelevant visual features ( ffg ) , altered visuospatial processing ( precuneus ) , altered sensory integration ( stg and supramarginal ) , altered bottom - up attention control ( ofc ) , altered feedback processing and top - down attention control ( mefg and mfg ) , and altered synchronization between bottom - up and top - down attention control ( antins ) .
the fmri data from the large - reward with feedback vswm task had the smallest contribution to adhd classification ( 57.5% accuracy , with only a trend toward a significant model fit ; fig . 
altered activity in adhd in this vswm task was evident in pc-4 ( fig . 
3 ) , which included the right inferior parietal lobe ( right - ipl ) , the right - antins , the right - mefg ( the activated right - mefg froi ; see fig .  2 ) and the right precuneus . these frois partially
overlap ( right - antins and right - mefg ) with those included in pc-3 ( small - reward with feedback ) .
this implies that the right - antins and right - mefg are associated with altered feedback processing in adhd ( regardless of reward expectation ) .
this is consistent with earlier findings showing that the right - antins and the right - mefg ( together with the anterior cingulate ) mediate between the central executive network and brain regions involved in risk / gain prediction , where response selection is required ( menon and udin , 2010 ; preuschoff et al . , 2008 ;
early studies showed that children with adhd exhibit poor cognitive control associated with lower levels of neural activity in the anterior insula , as compared with tds ( cubillo et al .
we show that using fmri data from four distinct vswm tasks enabled substantially better classification accuracies than the use of fmri data from a single vswm task .
nevertheless , the current investigation was limited to the manipulation of feedback and reward in vswm tasks , where many characteristics of the four tasks were identical .
it is possible that an even better adhd classification can be achieved by using other , perhaps more demanding cognitive tasks that require other cognitive skills impaired in adhd ( e.g. , tasks with specifically greater response inhibition demands ; booth et al . , 2005 ; rubia et al . , 2005
moreover , the duration of each vswm task we used here was 48 trials long ( ~100  s ) .
it is possible that these tasks can be shortened ( e.g. , reduced to 32 , or even fewer trials ) without compromising classification accuracies . as part of the development of a practical diagnosis tool , future investigations
should aim to identify an optimized ensemble of cognitive tasks that would yield the highest classification accuracies in the shortest scanning time possible .
future investigation may also involve looking for an ensemble of tasks that enable differentiation between children with adhd from other clinical populations , with shared symptoms .
the use of multi - task - based fmri data may also enable earlier diagnosis of adhd , prior to the onset of clear behavioral symptoms , which in turn may enable earlier intervention . in order to be of practical clinical use
, task - based fmri diagnosis requires the scanned participants to accurately perform a cognitive task while keeping still throughout a relatively long scan .
thus , a multi - task - based fmri diagnosis session may not be practical for very young children or individuals with severe cognitive deficits .
however , mild adhd and moderate adhd are much more common and represents more of a diagnostic dilemma for clinicians .
in contrast , severe or very early onset adhd would likely have a more distinct etiology .
future developments should ideally involve an integrative use of multi - task - based fmri , resting - state fmri and structural imaging .
this would likely enable an effective diagnosis of most clinical cases , and the detection of the onset of some clinical condition in early childhood .
ideally , future imaging - based diagnostic tools may enable finer differentiation of adhd subgroups , assisting clinicians in customizing intervention .
it is likely that similarly high ( or even higher ) classification accuracies can be achieved by using other machine - learning based classification methods .
future studies should specifically explore machine - learning methods based on whole brain data , from multiple tasks , which automatically detect brain regions in which the targeted clinical population exhibits abnormal patterns of brain activity ( e.g. , ryali et al .
we show that fmri data acquired while participants perform a few distinct cognitive tasks enables substantial improvement in the detection of adhd cases , as compared with the use of fmri data from a single task ( or corresponding behavioral data ) .
showing that fmri data enables better classification accuracies than the corresponding behavioral data suggests that even adhd cases that exhibited normal - like vswm performance were likely to be characterized by substantially altered pattern of brain activation .
this provides a proof - of - concept that scanning subjects while they perform an ensemble of distinct cognitive tasks is likely to payoff , enabling greater accuracies and higher confidence diagnosis of clinical cases .
we suggest that this approach can be used for diagnosing other clinical populations , and possibly also for dissociating between distinct clinical populations who share behavioral symptoms .
this would require using cognitive tasks that target the neurocognitive deficits characterizing the clinical condition of interest , or a battery of tasks that may enable dissociating between distinct neurocognitive abnormalities .
 finding neurobiological markers for neurodevelopmental disorders , such as attention deficit and hyperactivity disorder ( adhd ) , is a major objective of clinicians and neuroscientists . 
 we examined if functional magnetic resonance imaging ( fmri ) data from a few distinct visuospatial working memory ( vswm ) tasks enables accurately detecting cases with adhd . 
 we tested 20 boys with adhd combined type and 20 typically developed ( td ) boys in four vswm tasks that differed in feedback availability ( feedback , no - feedback ) and reward size ( large , small ) . 
 we used a multimodal analysis based on brain activity in 16 regions of interest , significantly activated or deactivated in the four vswm tasks ( based on the entire participants ' sample ) . 
 dimensionality of the data was reduced into 10 principal components that were used as the input variables to a logistic regression classifier . 
 fmri data from the four vswm tasks enabled a classification accuracy of 92.5% , with high predicted adhd probability values for most clinical cases , and low predicted adhd probabilities for most tds . 
 this accuracy level was higher than those achieved by using the fmri data of any single task , or the respective behavioral data . 
 this indicates that task - based fmri data acquired while participants perform a few distinct vswm tasks enables improved detection of clinical cases . 
Introduction
Materials and methods
Results
Discussion