The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)

Deep Transformation Method forDiscriminant Analysis of Multi-Channel Resting State fMRI

Abhay M S AradhyaSchool of Computer Engineering

NTU, Singaporeabhay003@e.ntu.edu.sg

Aditya JoglekarComputer Science Department

UCLA, USAadivj123@gmail.com

Sundaram SureshDepartment Of Aerospace

IISC, Indiasureshsundaram@iisc.ac.in

M. PratamaSchool of Computer Engineering

NTU, Singaporempratama@ntu.edu.sg

Abstract

Analysis of resting state - functional Magnetic ResonanceImaging (rs-fMRI) data has been a challenging problemdue to a high homogeneity, large intra-class variability,limited samples and difference in acquisition technolo-gies/techniques. These issues are predominant in the case ofAttention Deficit Hyperactivity Disorder (ADHD). In this pa-per, we propose a new Deep Transformation Method (DTM)that extracts the discriminant latent feature space from rs-fMRI and projects it in the subsequent layer for classificationof rs-fMRI data. The hidden transformation layer in DTMprojects the original rs-fMRI data into a new space using thelearning policy and extracts the spatio-temporal correlationsof the functional activities as a latent feature space. The sub-sequent convolution and decision layers transform the latentfeature space into high-level features and provide accurateclassification. The performance of DTM has been evaluatedusing the ADHD200 rs-fMRI benchmark data with cross-validation. The results show that the proposed DTM achievesa mean classification accuracy of 70.36% and an improve-ment of 8.25% on the state of the art methodologies was ob-served. The improvement is due to concurrent analysis of thespatio-temporal correlations between the different regions ofthe brain and can be easily extended to study other cognitivedisorders using rs-fMRI. Further, brain network analysis hasbeen studied to identify the difference in functional activitiesand the corresponding regions behind cognitive symptoms inADHD.

IntroductionBlood Oxygen Level Dependent (BOLD) fMRI is widelyused to map the brain functional activities (Song and Lu2017). The correlations between the regional functional ac-tivities during a cognitive task are studied using the multi-channel time series fMRI data to evaluate the interactionsCopyright c© 2019, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

between the various regions of the brain (Yousefnezhad andZhang 2017). fMRI data have been used to understand theetiopathogenesis of neurological disorders such as Atten-tion Deficit Hyperactivity Disorder (ADHD) (Mahanand,Savitha, and Suresh 2013), autism (Subbaraju et al. 2017)and epilepsy (Gill, Mirsattari, and Leung 2017). Studies onthe automatic diagnosis of neurological disorders from fMRIdata have focused on analyzing the correlations between theregions to find discriminant activities. Among them, SpatialFiltering Method (SFM) (Subbaraju et al. 2017) proposed aspatial transformation method to obtain discriminatory fea-tures by projecting the fMRI data into a new dimension,such that the two classes are highly separable. Their resultsrepresent the state of the art diagnostic performance on theAutism dataset. Recently, (Aradhya et al. 2018) utilized aregularized SFM based technique to improve the accuracyin the automatic detection of accuracy of ADHD from rs-fMRI data. SFM and its deravative techniques have adoptedthe ‘Fukunaga-Koontz ‘transform (Fukunaga 2013) to math-ematically derive the spatial transformation filter which isgreatly dependent on the mean distribution of the trainingdata. Further, the mean co-variance based spatial transfor-mation methodologies such as SFM overlooks the temporalinformation of the data, resulting in plausible loss of dis-criminative information.

Deep learning algorithms have recently gained impor-tance due to their ability to extract ingenious discrimina-tory information from highly homogeneous data. With rapidadvancements in computational power and parallelizationtechniques along with the increasing availability of large-scale dataset, there has been an increased effort to better un-derstand the human body using deep learning methodolo-gies. (Ravı et al. 2017), provided a brief overview of recentworks utilizing deep learning approaches in health infor-matics. Deep learning methodologies have especially beenadvantageous in investigating neurological and psycholog-

2556

ical disorders (Vieira, Pinaya, and Mechelli 2017). The re-sults indicate that deep learning methodologies achieve bet-ter classification in comparison to the traditional methods.However, direct application of deep learning methodologieson neurological datasets is a challenge due to - the smallsize of the dataset, imbalanced class distribution, high vari-ability in the data and lack of discriminatory informationbetween the classes. Deep learning approaches have beenexplored in the diagnosis of ADHD, (Kuang and He 2014)used Restricted Boltzmann Machines (RBM) for classifica-tion of ADHD. They converted the fMRI time series into a1D spectral feature vector and then trained a Deep BeliefNetwork (DBN) for the classification task and reported aclassification accuracy below 50% on the ADHD200 bench-mark dataset.

Recently, there has been increasing interest in the applica-tion of Convolution Neural Networks (CNNs) in the detec-tion and identification of biomarkers of diseases from fMRI,X-ray images, computed tomography scans and positronemission tomography scans (Ravı et al. 2017). (Riaz et al.2017) adopted a CNN based feature extraction method toidentify the discriminant functional activities using rs-fMRIand phenotypic information. They obtained an average ac-curacy of 62%, with a Support Vector Machine (SVM) clas-sifier. (Zou et al. 2017) introduced a multi-modal 3D CNNapproach to study ADHD. Here, the encoded structural-MRIand fMRI are jointly extracted as features and achieved aclassification accuracy of 69.15%. In (Vieira, Pinaya, andMechelli 2017), the functional connectivity in the brain be-tween the different regions was examined and the resultsconcluded that the functional connectivity between the var-ious regions of the brain encodes critical information inunderstanding ADHD. However, directly applying existingCNN algorithms to diagnose ADHD is not feasible due tothe small size of the publicly available ADHD dataset andthe lack of separability between the two classes. Motivatedby the findings in the above studies and to address the chal-lenges in the automatic diagnosis using rs-fMRI data, in thispaper, we develop a new deep learning method to effectivelycapture the spatio-temporal functional correlations betweenthe different regions of the brain.

In this paper, we propose a Deep Transformation Method(DTM), which project the rs-fMRI time series data into la-tent feature space using hidden transformation layer and thesubsequent convolution/decision layers help in classifica-tion. The cross-correlations across the different regions areused by hidden transformation layer to capture the spatio-temporal correlations between the regions of the brain. Fur-ther, the convolution layer transforms the latent space tohigh-level features. A softmax activation function in the de-cision layer estimates the class conditional probabilities toclassify the rs-fMRI data. DTM shows that the functionalcorrelations and convolution filters are complementary, andnot only help in extracting the spatio-temporal correlationsin the multi-channel data but also boost the classificationperformance.

The three significant contributions of this paper are:1. The hidden transformation layer in DTM handle the ho-

mogeneity and provide discriminant analysis by preserv-

ing the spatial-temporal information. Further, DTM issuitable to handle transformation from a small samplesize, and uncertainties in time-series data due to differ-ences in data acquisition methods.

2. We find that the functional activity disruptions inADHD are spatio-temporally correlated and the proposedmethodology provides support to the brain network dys-function hypotheses proposed to explain dysfunctionalfunctional brain activities in ADHD.

3. The proposed methodology achieves a state of the artclassification accuracy of 70.36% on a hold-out testingdataset from the ADHD200 consortium dataset, demon-strating the significance of concurrently analyzing thespatio-temporal information in fMRI for the diagnosis ofneurological disorders.The rest of the paper is organized as follows, first, an

overview of the problem, its implications and a brief liter-ature review is presented. Further, a detailed methodologyof the proposed Deep Transformation Method (DTM) is de-scribed. The results and discussion section presents the in-ferences and classification performance of DTM using theADHD200 dataset. In the next section, a brief overview ofthe experimental setup and dataset is presented. The perfor-mance of the DTM is evaluated using the state of the artmethodologies and the significance of the concurrent analy-sis of the spatio-temporal correlations are presented. Finally,the conclusions from the study are summarized and futurescope of work is defined.

Problem FormulationDiagnosis of ADHD from rs-fMRI is challenging due toa highly homogeneous dataset with large intra-class vari-ability. ADHD is one of the most prevalent developmen-tal neurological disorders in the world with (3-10% world-wide) (Burd et al. 2003). Traditional diagnostic methods forADHD are based on cognitive tests, interviews and obser-vations by the doctors and associates. However, these arehighly prone to variations due to human errors like inaccu-rate observations and inter-observer variability. The intelli-gence quotient, culture and language of the subject is alsoknown to have a major influence on the diagnostic proce-dure. These discrepancies in the current diagnostic proce-dure have led to a high risk of misdiagnosis and administra-tion of improper medications.

To overcome the shortcomings of the traditional diagnos-tic procedures and to have a better understanding of theetiopathogenesis of ADHD, researchers have looked at theBlood Oxygen Level Dependent (BOLD) functional Mag-netic Resonance Imaging (fMRI) to find discriminant brainactivities in children with ADHD. Task based fMRI stud-ies have shown variations in functional activity in variousbrain networks such as the default mode network (Konradand Eickhoff 2010), cingulo-fronto-parietal network (Bush2011), central executive network and the salience network(SN) (Menon 2011). However, a clear consensus on thepathophysiological cause of ADHD was not achieved dueto variations in the tasks and the resulting activation regionsbetween the studies. Therefore, researchers have looked at

2557

Figure 1: Architecture of the proposed DTM for the diagnosis of ADHD from rs-fMRI data.

resting state fMRI (rs-fMRI) which measures the intrinsicbrain activities to underline the causal factors of ADHD.rs-fMRI although independent from task-directed cognitiveprocesses, are extremely homogeneous and presents a chal-lenge in identifying the discriminant functional activities.

(Mahanand, Savitha, and Suresh 2013) studied the re-gional anatomy of the brain using the amygdala and cerebel-lar vermis regions. They used a meta-cognitive learning clas-sifier and achieved a classification accuracy of 65%. Analyz-ing rs-fMRI activity from specific regions of the brain leadsto a potential loss of valuable discriminative information.(Anderson et al. 2014) used a decision tree based approachto achieve a maximum classification accuracy of 66.8% us-ing both the phenotypic and rs-fMRI data. However, theclassification accuracies of studies focusing of automated di-agnosis of ADHD from rs-fMRI alone (without using phe-notypic information or predetermined neural regions) havebeen substantially lower. (Ghiassian et al. 2013) used a his-togram based feature reduction technique along with an Sup-port Vector Machine (SVM) classifier and achieved a clas-sification accuracy of 62.81%. (Guo et al. 2014) studied theregional connectivity between the regions of the brain andreported an accuracy of 63.75%. The existing approaches isnot able to handle high homogeneity, large intra-class vari-ability, limited samples and difference in acquisition tech-nologies/techniques.

Deep Transformation Method (DTM)

DTM is a deep learning based classifier that projects thers-fMRI data into a latent space and captures the spatio-temporal correlations to handle the above-mentioned issuesand understand the discriminant functional activities of thebrain. For this purpose, we propose a multi-layered DeepTransformation Method (DTM). The schematic representa-tion of the DTM architecture is shown in Figure 1. DTMuses the Hidden Transformation Layer (HTL) to project thers-fMRI data into a latent space where the classes are highlyseparable. The convolution layers extract high-level featuresfrom the grey scale correlation images and the decision layeruses a sigmoid activation function to classify the data.

Hidden Transformation Layer (HTL)

The hidden transformation layer transforms the time seriesrs-fMRI data Xi = {X1

i , X2i , X

3i , · · · , X90

i } (annotated inaccordance to the automated anatomical labeling template)into an image data by preserving spatio-temporal informa-tion. These transformed images are highly discriminative forboth the classes. Conventionally the transformation filtersare determined using mathematical derivations such as the‘Fukunaga-Koontz ‘transform (Fukunaga 2013). Previousstudies (Subbaraju et al. 2017) have used the derivative ap-proaches and have achieved significant improvement in clas-sification accuracy. However, the classification performanceof such deterministic methodologies are greatly reliant onthe accurate estimation of the spatial filter W ∈ R90×90, thattransforms the BOLD rs-fMRI time series data X ∈ R90×T

(from the 90 regions of the brain with T time points). Thedifferent classes of the rs-fMRI data are highly separable inthe new space and the transformed rs-fMRI data Y ∈ R90×T

calculated as

Y =WX (1)

Although the eigen value decomposition based ap-proaches provide a good approximation of the transforma-tion filter, they are highly reliant on the mean distributionof the data. Also, such methods are susceptible to noise inproblems with small dataset and high intra-class variabilityresulting in loss of discriminatory information. The discrim-inatory functional activities of the brain in the transformedrs-fMRI are represented as differences in functional con-nectivity between the different regions of the brain (Vieira,Pinaya, and Mechelli 2017). Therefore, it is beneficial to de-velop a non-deterministic approach to study the correlationsbetween the rs-fMRI signals in order to validate the discrim-inant activities of the brain.

DTM proposes a new deep learning based spatial trans-formation method, that projects the rs-fMRI data using alearn-able hidden transformation layer while preserving thespatio-temporal information using a cross-correlation basedregional connectivity estimation. In DTM, the regional con-nectivity matrix is estimated as

2558

S(i,j) =Yi[t]Yj [t− d]′

trace(Yi[t]Yj [t− d]′)(2)

The above equation can be simplified as

S(i,j) =Wi[t]Xi[t]Xj [t− d]′Wj [t− d]′

trace(Wi[t]Xi[t]Xj [t− d]′Wj [t− d]′)(3)

where, d = −tn, · · · , tn and i, j = 1, 2, · · · , 90

In Equation 3, tn represents the total number of timepoints in the time series data X, d is the delay used in cal-culating the cross-correlations while i and j are indices rep-resenting a pair of regions. The regional covariance matrixS ∈ R90×90×tn is a non-symmetric three dimensional ma-trix where 0 ≤ ||S|| ≤ 1. In order to determine the regionalconnectivity, it is beneficial to analyze the cross-correlationbetween the rs-fMRI time series signals as cross-correlationeffectively captures the temporal correlations between thevarious regions of the brain, which is otherwise lost duringsimple linear correlation based estimation.

Further, as Equation 3 can be represented as a seriesof linear matrix operations and therefore is differentiablewith respect to W. The transformation filter W is thereforeupdated using the backpropagation based learning strategysuch that error in classification performance is minimized.Therefore, DTM uses a data-centric deep learning approachto derive the transformation filter W to optimize the classifi-cation performance. Moreover, this enables the formulationof HTL as an independent deep learning layer which can beused in a multi-layer deep learning network to transform thedata into a latent feature space.

In this paper, we have applied a multi-layer convolutionalnetwork along with the HTL transformation layer in orderto extract high-level features from the rs-fMRI data. Con-volution neural networks use two-dimensional filters andtherefore necessitate the conversion of the three-dimensionalregional covariance matrix into a two-dimensional featurespace. Hence, the mean (Sm) and variance (Sv) across thetime dimension was calculated as

Sm =1

(2tn − 1)

(2tn−1)∑t=1

S(t) (4)

Sv = var(S(t)

)(5)

where, Sm and Sv are non-symmetric matrices of dimen-sion 90 × 90 which encapsulate the spatio-temporal func-tional activity between the 90 regions of the brain.

Convolutional LayerThe functional relationship between the different regionsof the brain is represented by the correlations between theneighbouring (spatially correlated) elements in the regionalcovariance matrices (Vieira, Pinaya, and Mechelli 2017).

Convolutional neural networks is a popular deep learningmethod that is adept at capturing spatial information and hasbeen effective in the classification of natural images (Le-Cun, Bengio, and Hinton 2015). DTM exploits the convo-lutional layers to extract low-level spatial features and trans-form them into high-level features. The regional connectiv-ity matrices Sm and Sv are converted into normalized meangreyscale image Gm and the variance greyscale image Gv

are given as

Gm = 0.5 (Sm + 1) (6)

Gv = 0.5 (Sv + 1) (7)

Gm andGv are used as inputs to train the two convolutionlayers. Convolution operations are generally data intensive,wherein the accuracy is directly proportional to the quantityof data used to train the network and the number of layers(depth) used.

However, neurological datasets pose a challenging prob-lem as the number of data samples available are generallylimited. Using a large number of convolution layers wouldthus lead to over-fitting, resulting in a bad generalization ofthe data. Therefore, DTM uses a shallow LeNet (LeCun andothers 2015) based architecture with two layers of Conv-RELU-Maxpool followed by a decision layer.

The first convolutional layer consists of eight 4× 4 filterswith a depth two (corresponding to the 2 input channels).A stride of one was adopted in both horizontal and verticaldirections to ensure that none of the discriminatory infor-mation is neglected. In order to maintain the dimension ofthe inputs, they are padded with zeros (’SAME’ padding).Further, a Leaky Rectified Linear Units (ReLU) layer withα = 0.2 was used to add non-linearity to the network. Amax-pooling layer with a 8 × 8 filter and 8 × 8 stride wasintroduced which although leads to some information loss,prevents having too many learnable parameters compared tothe size of the dataset to avoid overfitting. The second con-volution layer had more depth along with narrower filters asper the standard practice. It consisted of sixteen 2× 2 filterswith 8 input channels with a unit stride, followed by a leakyReLU (α = 0.2) non-linearity and a max-pooling layer with4× 4 filter and 4× 4 stride.

Decision LayerThe decision layer consists of a fully connected layer anda softmax layer. The output from the last Conv-RELU-Maxpool layer is connected to a fully connected layer withabout 1000 neurons, which are then connected to the soft-max layer with two output neurons. The softmax layer witha sigmoid activation function was used to estimate the condi-tional probability P (Cx|X); that is, the confidence of DTMin predicting the class of the current input X correctly as Cx.The class with the maximum conditional probability wasgiven as the predicted class for the input X.

Brain Functional Activity MapsThe activity maps of the brain are useful in analyzing thedifferences in functional activities in the regions of the brain

2559

between subjects with neurological disorders and neurotyp-ical subjects. The differences in neural activity are obtainedusing the inverse of the spatial filter W, derived such thatthe original time series can be regenerated from the inversespatial filter (W−1) and the projected time series data Y as

X =W−1Y (8)

where X is the regenerated BOLD rs-fMRI data. Thecolumns of W−1 is referred to as spatial distributions. Eachelement in the column of W−1 is called as a spatial weightand is assigned to each of the corresponding of the 90 Au-tomated Anatomical Labelling (AAL) regions. The spatialweights with significant variance from the mean of each col-umn highlights the significant regions with differences inBOLD rs-fMRI signals between the classes. The polarity ofthe spatial weights do not hold any significance, and only theabsolute value of the spatial weight needs to examined. Themean spatial distribution and standard deviation of each ofthe spatial distribution were calculated and the regions withspatial weights whose magnitude is greater than two timesthe standard deviation away from the mean were consideredsignificant in this paper. The inverse spatial filter from thetrail with the highest classification accuracy was chosen anda representative spatial distribution is plotted and the dis-tribution of the spatial weights are analyzed. The regionsare further visualized using a brain functional map to depictthe regions with discriminant functional activity between theclasses.

Experimental SetupThe deep transformation method was implemented as a”TensorFlow” graph in ”Python” scripting language and wastrained over one hundred (100) iterations with a learning rateof 0.001 using a mini-batch gradient descent approach with abatch size of 32. The batch learning approach prevents con-vergence is a trade-off between avoiding local minima andthe convergence time. Although the DTM took thirty min-utes (30) of training time using an ’Intel(R) Xeon(R) CPUE5-1630 v4 @ 3.70GHz’ central processing unit, it signif-icantly reduced to seven (7) minutes with the use of ’TeslaP100-SXM2-16GB’ graphical processing unit. Each fold ofdata consisted of non-intersecting training and testing datasamples drawn in the ratio 371:54. Every consecutive foldof test data was ensured to have no overlap with the pre-vious test data to obtain generalized results and the aver-age performance over the five folds are reported as the meanclassification performance measure.

ADHD200 DatasetIn order to validate the performance of the DTM, we haveused the benchmark ADHD200 dataset from the Interna-tional Neuroimaging Data-sharing Initiative (INDI). INDIhas facilitated Deep learning research on ADHD by aggre-gating rs-fMRI data from 947 people between the age of 7years to 22 years, aggregated from 8 different institutionsunder the ADHD200 consortium. However, for the studypresented in this paper the data from Brown University wereexcluded as the diagnostic information of the rs-fMRI data

were not available at the time of compilation of this study.Of the 947 people in the ADHD200 consortium dataset, 563people (59.45%) were found to male right-handed. Previ-ous studies (Skounti, Philalithis, and Galanakis 2007) haveshown that the gender and handedness of a person severelyinfluence the presentation of functional brain activities in rs-fMRI data. Hence, in order to preserve the homogeneity inthe data, this study is confined to male right-handed people.

The rs-fMRI data from the ADHD200 consortium waspre-processed using the ATHENA pipeline (Bellec et al.2017) in order to remove the variations due to the physio-logical noise, head motion and scanner drifts. The denoisedrs-fMRI data without bandpass filtering provided by (Bellecet al. 2017) was anatomized using the AAL template into116 regions. The 26 regions corresponding to the cerebel-lum were excluded to minimize the effects of involuntaryactivities such as breathing and cardiac activity. Finally, amanual quality control (QC) check was performed and sam-ples with incomplete data and that failed the QC in (Bellec etal. 2017) were removed and rs-fMRI data from the 90 AALregions were obtained from 465 people. Further details onthe preprocessing and QC measures can be obtained from(Bellec et al. 2017).

To facilitate cross-validation and evaluate the perfor-mance of the algorithm, the dataset was partitioned intotraining and test data-sets of 371 and 94 subjects respec-tively. A 10-fold random partition was done ensuring theratio of ADHD to TDC remained constant across the folds.The mean classification accuracy and the standard deviationacross the partitions are considered as the key indicators ofthe performance of DTM.

Results and DiscussionIn this section, the diagnostic performance of the DTM inclassifying ADHD from BOLD rs-fMRI time series datahas been presented. The performance is measured in termsof accuracy, sensitivity, specificity and F-score of the clas-sification. First, we compare the performance of the pro-posed DTM with previous studies on the automatic diag-nosis of ADHD in Table 1. Studies (Ghiassian et al. 2013;Guo et al. 2014) on the diagnosis of ADHD using BOLDfMRI data from the ADHD200 consortium dataset utilizingvarious feature extraction algorithms and a linear classifierlike SVM have achieved a maximum accuracy of 63.75%.However, (Mahanand, Savitha, and Suresh 2013) used ametacognitive classifier to achieve the best performance of65%. Using phenotype data along with BOLD fMRI dataimproved the classification accuracy. (Anderson et al. 2014)explored a decision tree based approach to achieve a clas-sification accuracy of 66.8%, whereas (Riaz et al. 2017)adopted a CNN based process in order to extract the func-tional connectivity as features and then evaluated them usingan SVM classifier to obtain an accuracy between 63.4% and68.6%. Also, (Riaz et al. 2017) used data from individualacquisition sites in order to validate the results in order toavoid inter-site variability. (Zou et al. 2017) made use of thes-MRI and rs-fMRI data to extract six types of 3D featuresand introduced a 3D CNN based classifier. They achieved amaximum classification accuracy of 69.15%. These results

2560

Table 1: Comparison of diagnostic performance between the proposed DTM and state of the art methodologies using ADHD200consortium dataset

Reference Data Classifier Testingaccuracy

(Ghiassian et al. 2013) BOLD rs-fMRI SVM 62.81%(Guo et al. 2014) BOLD rs-fMRI SVM 63.75%

(Mahanand, Savitha, and Suresh 2013) BOLD rs-fMRIMeta-cognitive Radial

65%Basis Function

(Anderson et al. 2014) BOLD rs-fMRI and phenotype data Decision tree 66.8%(Riaz et al. 2017) BOLD rs-fMRI and phenotype data SVM 63.4% - 68.6%(Zou et al. 2017) BOLD rs-fMRI and s-MRI 3D CNN 69.15%

DTM BOLD rs-fMRI Softmax 70.36%

show the highest accuracy in diagnosis achieved by previ-ous studies. Although from the above results it is observedthat phenotype data and s-MRI data when used along withthe rs-fMRI data improves the classification performance,it hinders the derivation significant conclusions on the dis-criminant functional activities in ADHD. Therefore, DTMuses only BOLD rs-fMRI as input and in comparison yieldsa better diagnostic performance on a hold-out test dataset.DTM achieves a mean classification accuracy of 70.36%with a low standard deviation of 0.02 which is a significantimprovement of 8.25% over the existing methods. The con-fusion matrix of the classification performance is given inTable 2.

Table 2: Classification performance of DTMParameter Train Percentage Test Percentage

True Positive 49.8% 41.5%True Negative 39.5% 28.9%False Positive 10.5% 13.7%False Negative 0.2% 15.9%

Evaluation of significance of concurrent analysis ofspatio-temporal information

Table 3: Comparison of diagnostic accuracy of DTM withdifferent region correlation methodologies

Region correlation Training Testingmeasure Mean S.D Mean S.DLinear correlation 95.12% 0.12 55.27% 0.01Cross-correlation 89.23% 0.007 70.36% 0.02

The advantages of substituting the linear correlation re-gional covariance matrix with the mean and variance of theregion covariance matrix derived using the cross-correlation

approach is shown in Table 3. Adopting the linear correla-tion approach to derive the regional covariance matrix al-though leads to an increase in mean training accuracy, themean testing accuracy sharply reduces to 55.27% due tooverfitting. These results indicate that the time series signalsof the different regions of the brain have higher correlationswhen shifted in time. These findings prove that the func-tional activity disruptions in ADHD are spatio-temporallycorrelated and supports the brain network dysfunction the-ory proposed by (Menon 2011).

Identification of distinguishable brain activities inADHD right handed male subjectsThe spatial weight distribution and brain activity maps of theregions of the brain highlight the discriminant regions of thebrain with significant differences in functional activity. Thespatial weights from the 90 regions of one of the prominentspatial distributions is plotted in Figure 2. The magnitudeof the spatial weights in the figure represent the importanceof the region for classification. As the signs of the spatialweights do not signify any information the absolute value ofthe spatial weights is considered. The magnitude of each ofthe spatial weights corresponds to the influence of the cor-responding regions functional activity in the classificationprocess.

From the Figure 2 it shows that the Rolandic Operculum(18), Medial Frontal Gyrus (23), Posterior Cingulate Gyrus(36), Hippocampus (38) and the Middle Temporal Gyrus(85) have significantly greater spatial weights. Therefore, itimplies that these regions of the brain display discriminativefunctional activity among male right-handed subjects diag-nosed with ADHD. The corresponding brain functional ac-tivity map is presented in Figure 3, where the regions of thebrain are marked by their AAL region number. Prominentdifferences in functional activity are observed in the tem-poral lobe and the posterior cingulate cortex. The temporallobe is primarily responsible for the long-term memory (Si-mons and Spiers 2003), while the posterior cingulate cortexis an integral part of the Default Mode Network (DMN) andacts as the functional link between the prefrontal and pari-etal regions of the brain (Fransson and Marrelec 2008). It is

2561

Figure 2: AAL regions exhibiting discriminant functional activity and the associated spatial weights obtained from DTM

Figure 3: Brain functional activity map showing the lateral and dorsal view of the brain, presenting the regions of the brain withsignificant variances in rs-fMRI signal between ADHD patients and neurotypical malesNote: Rolandic Operculum (18), Medial Frontal Gyrus (23), Posterior Cingulate Gyrus (36), Hippocampus (38) and the MiddleTemporal Gyrus (85)

observed that the regions identified with discriminant func-tional activity by DTM are related to the cognitive symp-toms associated with ADHD such as hyperactivity, impul-sivity and inattentiveness.

ConclusionsThe proposed deep transformation method uses a hiddentransformation layer to project the rs-fMRI into discrimi-native latent space and preserve the spatio-temporal infor-mation. Further, the convolution layers and decision layer

helps in accurate classification. The experimental study us-ing ADHD200 right-handed male dataset clearly shows thatthe proposed DTM method are higher (approximately 8%)when compared to the existing results reported in the lit-erature. DTM extracts the spatio-temporal correlations infunctional activities of the brain instead of the simple spa-tially correlated functional activities in order to train theCNN based classifier. The results demonstrate that ADHDis characterized by differences in connectivity between thedifferent regions of the brain and therefore should be treated

2562

as a brain network dysfunctional neurological disorder. Thebrain network analysis reveals that the difference in func-tional activities is predominant in the temporal lobe and pos-terior cingulate cortex which implies inattentiveness and im-pulsivity in ADHD.

ReferencesAnderson, A.; Douglas, P. K.; Kerr, W. T.; Haynes, V. S.;Yuille, A. L.; Xie, J.; Wu, Y. N.; Brown, J. A.; and Cohen,M. S. 2014. Non-negative matrix factorization of mul-timodal mri, fmri and phenotypic data reveals differentialchanges in default mode subnetworks in adhd. NeuroImage102:207–219.Aradhya, A. M. S.; Subbaraju, V.; Sundaram, S.; and Sun-dararajan, N. 2018. Regularized spatial filtering method(r-sfm) for detection of attention deficit hyperactivity disor-der (adhd) from resting-state functional magnetic resonanceimaging (rs-fmri). In 2018 40th Annual International Con-ference of the IEEE Engineering in Medicine and BiologySociety (EMBC), 5541–5544.Bellec, P.; Chu, C.; Chouinard-Decorte, F.; Benhajali, Y.;Margulies, D. S.; and Craddock, R. C. 2017. The neurobureau adhd-200 preprocessed repository. Neuroimage144:275–286.Burd, L.; Klug, M. G.; Coumbe, M. J.; and Kerbeshian,J. 2003. Children and adolescents with attention deficit-hyperactivity disorder: 1. prevalence and cost of care. Jour-nal of Child Neurology 18(8):555–561.Bush, G. 2011. Cingulate, frontal, and parietal cortical dys-function in attention-deficit/hyperactivity disorder. Biologi-cal psychiatry 69(12):1160–1167.Fransson, P., and Marrelec, G. 2008. The pre-cuneus/posterior cingulate cortex plays a pivotal role in thedefault mode network: Evidence from a partial correlationnetwork analysis. Neuroimage 42(3):1178–1184.Fukunaga, K. 2013. Introduction to statistical pattern recog-nition. Elsevier.Ghiassian, S.; Greiner, R.; Jin, P.; and Brown, M. 2013.Learning to classify psychiatric disorders based on fmr im-ages: Autism vs healthy and adhd vs healthy. In Proceedingsof 3rd NIPS Workshop on Machine Learning and Interpre-tation in NeuroImaging.Gill, R. S.; Mirsattari, S. M.; and Leung, L. S. 2017. Restingstate functional network disruptions in a kainic acid modelof temporal lobe epilepsy. NeuroImage: Clinical 13:70–81.Guo, X.; An, X.; Kuang, D.; Zhao, Y.; and He, L. 2014.Adhd-200 classification based on social network method.In International Conference on Intelligent Computing, 233–240. Springer.Konrad, K., and Eickhoff, S. B. 2010. Is the adhd brainwired differently? a review on structural and functional con-nectivity in attention deficit hyperactivity disorder. Humanbrain mapping 31(6):904–916.Kuang, D., and He, L. 2014. Classification on adhd withdeep learning. In Cloud Computing and Big Data (CCBD),2014 International Conference on, 27–32. IEEE.

LeCun, Y., et al. 2015. Lenet-5, convolutional neural net-works. URL: http://yann. lecun. com/exdb/lenet 20.LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep learning.nature 521(7553):436.Mahanand, B.; Savitha, R.; and Suresh, S. 2013. Computeraided diagnosis of adhd using brain magnetic resonance im-ages. In Australasian Joint Conference on Artificial Intelli-gence, 386–395. Springer.Menon, V. 2011. Large-scale brain networks and psy-chopathology: a unifying triple network model. Trends incognitive sciences 15(10):483–506.Ravı, D.; Wong, C.; Deligianni, F.; Berthelot, M.; Andreu-Perez, J.; Lo, B.; and Yang, G. Z. 2017. Deep learning forhealth informatics. IEEE Journal of Biomedical and HealthInformatics 21(1):4–21.Riaz, A.; Asad, M.; Al-Arif, S. M. R.; Alonso, E.; Dima,D.; Corr, P.; and Slabaugh, G. 2017. Fcnet: a convolutionalneural network for calculating functional connectivity fromfunctional mri. In International Workshop on Connectomicsin Neuroimaging, 70–78. Springer.Simons, J. S., and Spiers, H. J. 2003. Prefrontal and medialtemporal lobe interactions in long-term memory. Nature Re-views Neuroscience 4(8):637.Skounti, M.; Philalithis, A.; and Galanakis, E. 2007. Varia-tions in prevalence of attention deficit hyperactivity disorderworldwide. European journal of pediatrics 166(2):117–123.Song, X., and Lu, H. 2017. Multilinear regression for em-bedded feature selection with application to fmri analysis.In AAAI, 2562–2568.Subbaraju, V.; Suresh, M. B.; Sundaram, S.; andNarasimhan, S. 2017. Identifying differences in brainactivities and an accurate detection of autism spectrumdisorder using resting state functional-magnetic resonanceimaging: A spatial filtering approach. Medical imageanalysis 35:375–389.Vieira, S.; Pinaya, W. H.; and Mechelli, A. 2017. Usingdeep learning to investigate the neuroimaging correlates ofpsychiatric and neurological disorders: Methods and appli-cations. Neuroscience & Biobehavioral Reviews 74:58 – 75.Yousefnezhad, M., and Zhang, D. 2017. Local discriminanthyperalignment for multi-subject fmri data alignment. InAAAI, 59–65.Zou, L.; Zheng, J.; Miao, C.; Mckeown, M. J.; and Wang,Z. J. 2017. 3d cnn based automatic diagnosis of attentiondeficit hyperactivity disorder using functional and structuralmri. IEEE Access 5:23626–23636.

2563