Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks.

Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.

Introduction

Neurodegenerative diseases have gained increasing attention in the past few decades; these include Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and others. Several research groups have tackled the usage of machine learning algorithms for the sake of detection, localization, prediction of the disease, or clustering different diseases or disease stages [1-3]. Image based diagnosis of AD is important and required mainly to avoid subjective assessments [4]. Deep learning-based methods gives successful results particularly in medical image analysis [5] due to flexible and efficient formulations [6].

In 2017, over 121 thousand people died from AD, in the United States, making it the sixth leading cause of death. Between the years 2000 and 2017, the number of deaths due to AD has increased by 145% [7]. By 2050, the number of people older than 60 years will be increased by 1.25 billion, equivalent to 22% of the global population, with 79% living in the world’s less developed countries [8]. The annual expenses for the disease per is around $868 and $3,109 per person in low-income and lower-to middle- income countries respectively [9].

MCI can be an antecedent to several neurodegenerative diseases [1,10]. Prominently, MCI is considered to be the prodromal phase to AD [7,11]. Around 15–20% of people elder than 65 years were diagnosed with MCI because of different pathologies. In a two-year follow-up, 15% of the subjects with MCI would develop dementia, and in a five-year follow-up 32% of subjects, with MCI, would develop AD [7].

Several research projects proposed machine learning algorithms assessing AD with different goals based on different types of features, including; Cerebrospinal Fluid (CSF) histopathology, cognitive questionnaire tests, and Medical Imaging, such as; Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) [1,12-17].

In this work, the objective was to classify AD and MCI from healthy controls (HC) using a convolutional neural network (CNN), where DTI and MRI were employed. Moreover, all diffusion maps were investigated and compared; Mean Diffusivity (MD), Fractional Anisotropy (FA), and Mode of Anisotropy (MO). Moreover, the effect of the time interval between two subsequent scans was investigated to assess the convenient period between one’s scans to avoid overfitting.

Materials and methods

The dataset, employed in this study, is owned by a third-party organization; the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A complete description of ADNI and up-to-date information is available at http://adni.loni.usc.edu/ and data access requests are to be sent to http://adni.loni.usc.edu/data-samples/access-data/. Detailed inclusion criteria for the diagnostic categories can be found at the ADNI website (http://adni.loni.usc.edu/methods, ADNI2 manual page 27). All ADNI studies are conducted according to the Good Clinical Practice guidelines, the Declaration of Helsinki, and U.S. 21 CFR Part 50 (Protection of Human Subjects), and Part 56 (Institutional Review Boards). Written informed consent was obtained from all participants before protocol-specific procedures were performed. The ADNI protocol was approved by the Institutional Review Boards of all of the participating institutions. The ethics committees/institutional review board that approved the ADNI study are listed within S1 File. The dataset employed is formed of 406 subjects: 185, 106, and 115 subjects with HC, MCI and AD respectively. The subjects’ characteristics are listed in Table 1.

Table 1

Subjects’ characteristics.

	Male/Female (no. unique)	No. scans separated at least a year or more	Age	MMSE	Years of Education
HC (n = 185)	89/96 (55)	110	73.6±6.1	29.0±1.2	16.4±2.7
MCI (n = 106)	66/40 (44)	59	73.3±5.8	26.8±1.9	16.3±2.6
AD (n = 115)	69/46 (50)	57	75.7±8.1	23.0±2.5	15.5±3.0

HC: Healthy controls, MCI: Mild cognitive impairment, AD: Alzheimer’s disease, MMSE: Mini-mental state exam[18]

The preprocessing of the scans was adopted from the pipeline introduced in [19,20]. MRI T1 scans were spatially segmented and normalized to the Montreal Neurological Institute (MNI) template using the Statistical Parametric Mapping (SPM12) software; specifically, the Computational Anatomy Toolbox (CAT12) was utilized and the Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL) algorithm was implemented [21]. Linear regression was implemented to remove the effect of the Total Intracranial Volume (TIV) [22]. This pipeline output several files; such as the White Matter (WM) volume, Gray Matter (GM) volume, TIV values, and the deformation fields (to and from the MNI space). On the other hand, the DTI scans were preprocessed as per the guidelines of the FMRIB Software Library (FSL) [23]; where the eddy currents were corrected, the skull was stripped, the diffusion tensor was calculated, and the diffusion maps were calculated. Last, the DTI maps were co-registered with the normalized T1 scans of the same subject at the same time point via the SPM coregister toolbox [24].

Three main maps of diffusion can be calculated from DTI, named; MD, FA, and MO [25,26]. MD is the average of the eigenvalues of the diffusion tensor ellipsoid [27]. FA is a measure of the flow in the axons being isotropic or closer to anisotropic (0 is perfect isotropy and 1 is perfect anisotropy). MO reflects the skewness of the flow; i.e. is it closer to tubal (-1), spherical (0), or planar (1) flow. In neurodegenerative diseases, including Alzheimer’s disease, the demyelination can be perceived as an increase in Radial Diffusivity (RD) and a decrease in FA [28,29].

The hippocampus and the entorhinal cortex are the main and earliest regions that develop anatomical atrophy in the case of AD [26,28,30-36]. Thus, the bounding box, including the hippocampus and the entorhinal cortex, was identified via the Harvard-Oxford [37-40] and the Juelich [41] atlases respectively; originally, the scans were 121×145×121 and by selecting the Volume of Interest (VOI), they became 61×37×38 (Fig 1).

Fig 1

The hippocampus and the entorhinal cortex bounding box.

To address the classification task, a 2D CNN was employed, since it has the advantage of taking into consideration the spatial relationships between pixels; especially with a pathology that would progress over time and brain regions [19,42-44].

The proposed CNN consisted of the image input layer, convolutional filters as described below, batch normalization layer, ReLU layer [45], maxpooling layer, fully connected layer, softmax layer, and the classification layer (Fig 2). The weights of the network were calculated using the gradient descent optimization using the Root Mean Square Propagation (rmsprop) algorithm [46]. Recently, Sobolev gradient based optimization has been used in deep network based methods to diagnose AD [47,48]. However, the standard gradient descent optimization is efficient in the proposed approach in terms of computation.

Fig 2

CNN architecture (FA maps as an example of input scans).

BN: Batch normalization, FC: Fully connected ayer.

The concept of 1×1 convolution was first introduced by Lin et al. [49], whereas its usage has been scarce in medical applications [50,51]. Its role in decreasing the complexity while increasing the nonlinearities -hence, the discriminative ability- was later clarified in [52].

In order to select the optimal network hyperparameters such as the network depth, filters’ size and number, iterative experiments were employed where one layer was added and its filter size was optimized before adding the next layer. The optimal sizes were selected when the highest performance measures were met. It is worth noting that the learning of the weights was done via mini-batch scheme; where the batch size, the learning rate, and the number of epochs were 20, 0.001 and 60 respectively. The ten-fold cross-validation implies 10% test set and 90% training set; further, the training set is split into 75% for the network training, and 25% for the validation of the parameters. Validation was performed once per epoch.

In this work, three experiments were performed:

Analysis of individual and cascaded maps: The MD volume was fed to CNN and the performance measures of the test set were calculated; the same was done for FA and MO and the optimal CNN parameters were selected. In addition, the three diffusion volumes were cascaded and fed to the CNN and the optimal CNN parameters were selected for the cascaded volume. The same setting was done for the cascaded MD and GM volumes. Cascading in this study was done by concatenating the diffusion map volumes following each other in depth. Thus, the original size of 61×37×38 for one map would increase to 61×37×76 and 61×37×114 in case of two and three maps respectively; where the third dimension is normal to the axial plane as described in Fig 2

Analysis while including a single scan per year for the same subject: The impact of excluding temporally-close scans (less than a year); i.e. portions vs. annual was assessed in this text. In particular, the dataset comprised subjects who have been scanned more than once; the interval between two subsequent scans was not fixed. Thus, all scans were explored altogether (denoted in this study by portions). In addition, scans that remained after excluding those belonging to the same subjects but scanned within less than a year; either from preceding or succeeding scan (denoted by annual) were explored as well. In other words, if subject X has scans XS1, XS2, and XS3 sorted by the date the scan was performed; where XS1 and XS2 were taken within less than a year, and XS2 and XS3 were taken within a year or more, only XS1 and XS3 would be kept for that subject X; which is referred as annual. Whereas, if all scans for X were retained irrespective of the interval; i.e. keeping XS1, XS2, and XS3, it would be referred as portions.

Analysis of segregated versus mixed training and test datasets: Separating the cross-validation folds by IDs or random assignment to any fold; segregated vs. mixed was evaluated in this text. In this analysis, the impact of multiple scans per subject was exploited in two ways; to put all scans belonging to one subject in either a training or test set per cross-validation which was referred to previously as “segregated”, or just to randomize the selection of scans per cross-validation irrespective of the ID of the subject which was referred as “mixed”.

Five performance measures were calculated; Area Under the Curve (AUC), accuracy, sensitivity, specificity, and F1- measure[53]. Since the MD mean of the performance measures was primarily the highest in comparison with the other maps, a statistically significant difference between all other maps and MD was analyzed. The Sign test was employed [54,55] since the population was not always normal, assessed by the Shapiro-Wilk test [56], and there were only ten points of observations (number of the cross-validation folds).

To plot the Receiver Operating Characteristic (ROC) curve for any experiment, ten-fold cross validation passes, having different x-y pairs (sensitivity/TPR and 1-specificity/FPR) corresponding to each cross-validation pass, were utilized. The ten curves were interpolated to a common x-axis, named False Positive Rate (FPR), and calculated the average for the other axis, named True Positive Rate (TPR).

For each fold in the cross-validation, the ROC curve has a set of FPR and TPR points forming the curve. First, a common arbitrary set of FPR values was chosen. In order to calculate the average ROC curve of the ten folds, the (FPR, TPR) pairs were sorted in a monotonically increasing fashion with respect to FPR. All the ten curves were looped over, where each loop was unique over the FPR values. To avoid the problem of multiple TPR-values for the same FPR value; i.e. vertical lines, the (FPR, TPR) pair were selected at the last value of FPR (corresponding to the largest value of TPR denoted by TPRmax for the same value of FPR). Then, the (FPR, TPRmax) was interpolated to the previously-selected common FPR-grid. The same method was applied for the rest of the curves, such that all of them coincide on the same grid of FPR, then the average was calculated.

The aim of this work was to provide an automatic classification of the MCI and AD versus HC. For some subjects having multiple scans at different timepoints, the effect of selecting only scans that were taken a year or more from the previous one with respect to the same subject was investigated. In addition, the impact of having different timepoint scans for the same subject in the training set and how the separation based on the subject is assessed in terms of the effect on the overall performance.

The implementation was done on a 64-bit Windows server 2019 machine, Intel Xeon CPU E5-2650 @ 2 GHz processor, eight cores, and 384 GB RAM. The CNN architecture was built using MATLAB ver. R2018b.

Results

In this study, several objectives were addressed for detecting AD via a machine learning technique; namely CNN. the first objective is to search for the best values for the CNN hyperparameters that would maximize performance. Whereas, the second objective is study if the diffusion maps would yield a good discrimination between different classes or fusion with other structural data will boost the performance. The third objective is to evaluate the impact of the time gap between two successive scans belonging to the same subject. Finally, the study was interested in assessing the effect of mixing the training and test sets or segregating them such that all scans belonging to the same subject are in either the training set or the test set.

Upon evaluating the different hyperparameters of 2D CNNs, the optimal CNN size, for one volume (MD, MO, FA, or GM) each of which is 61×37×38, is formed of one layer in depth having five filters each of which was 5×5×38. On the other hand, the optimal CNN size for cascaded volumes experiments; namely MD+MO+FA of size 61×37×114 and GM+MD of size 61×37×76, is formed of two layers; where the first included thirty 1×1×114 or thirty 1×1×76 filters respectively and the second layer included five 3×3×30 filters. Regarding 2D CNNs, it is worth pointing out that the depth of the filters must match with that of the input volumes, and that the depth of the output of the convolution must match with the number of filters [57].

Analysis of individual and cascaded maps

Regarding the maps themselves, the MD maps were roughly statistically significant than other diffusion maps, in comparison, and also the three volumes cascaded (Tables 2 and 3 respectively). MD maps resulted in a classification accuracy of 88.9% and 71.1%, a sensitivity of 83.5% and 51.9%, a specificity of 91.7% and 81.8% and AUC of 0.93 and 0.68 for classifying AD and MCI respectively from HC. In the experiments implemented in this work, the FA yielded better results than MO. FA resulted in an accuracy of 86% and 72.1%, a sensitivity of 78.7% and 50%, a specificity of 90.1% and 79.4%, and AUC of 0.88 and 0.73 for classifying AD and MCI respectively from HC. On the other hand, MO resulted in an accuracy of 82.8% and 64.4%, a sensitivity of 73.8% and 37.4%, a specificity of 87.9% and 79.4%, and an AUC of 0.88 and 0.62 for HC/AD and HC/MCI classification respectively. Feeding the GM to the proposed CNN improved the results but not significantly (Table 2). GM resulted in an accuracy of 91.3% and 75.7%, a sensitivity of 88.3% and 60.7%, and a specificity of 92.8% and 84% and an AUC of 0.96 and 0.80, for classifying AD and MCI respectively from HC.

Table 2

Classification results.

	Portions segregated					Annual segregated
	MD	Mode	FA	GM	MD and GM	MD	Mode	FA	GM	MD and GM
HC/AD
Acc	0.889±0.099(0.898)	0.828±0.089(0.830)	0.860±0.106(0.895)	0.913±0.077(0.913)	0.935±0.078 (0.965)	0.868±0.109(0.910)	0.814±0.090(0.789)	0.826±0.096(0.879) ‡	0.922±0.056(0.938)	0.904±0.049 (0.882)
AUC	0.931±0.083(0.969)	0.878±0.139(0.910) *	0.878±0.144(0.934)	0.955±0.058(0.977)	0.941±0.082 (0.972)	0.936±0.084(0.982)	0.858±0.116(0.833)	0.876±0.111(0.897)	0.976±0.041(1.000) */‡‡	0.974±0.036 (0.985)
Sens	0.835±0.202(0.905)	0.738±0.160(0.773)	0.787±0.212(0.818)	0.883±0.187(1.000)	0.925±0.087 (0.955)	0.857±0.077(0.833)	0.643±0.132(0.667) ***	0.680±0.150(0.667) **	0.910±0.096(0.917)	0.893±0.093 (0.833)
Spec	0.917±0.129(1.000)	0.879±0.104(0.889)	0.901±0.089(0.889)	0.928±0.111(1.000)	0.939±0.112 (1.000)	0.873±0.150(0.909)	0.900±0.117(0.955)	0.900±0.117(0.909)	0.927±0.094(1.000)	0.909±0.086 (0.909)
F1	0.840±0.149(0.861)	0.755±0.131(0.766)	0.796±0.167(0.869)	0.876±0.118(0.895) *	0.918±0.092 (0.950) *	0.827±0.123(0.861)	0.704±0.130(0.641) **	0.727±0.144(0.775) **/‡	0.891±0.071(0.889)	0.867±0.061 (0.857) ‡
HC/MCI
Acc	0.711±0.170(0.737)	0.644±0.154(0.650) *	0.721±0.116(0.719)	0.757±0.130(0.754)	0.796±0.139 (0.776) *	0.703±0.132(0.735)	0.638±0.094(0.647)	0.632±0.181(0.706) *	0.745±0.115(0.765) **	0.722±0.139 (0.735)
AUC	0.681±0.247(0.697)	0.619±0.193(0.587)	0.732±0.171(0.740)	0.800±0.159(0.860)	0.842±0.124 (0.852)	0.644±0.232(0.583)	0.730±0.141(0.705)	0.648±0.175(0.652)	0.745±0.157(0.758) *	0.773±0.129 (0.795)
Sens	0.519±0.215(0.500)	0.374±0.323(0.286) *	0.499±0.249(0.450)	0.607±0.307(0.427) *	0.627±0.298 (0.527) *	0.490±0.238(0.500)	0.207±0.225(0.167) **	0.323±0.168(0.333)	0.487±0.194(0.500)	0.607±0.169 (0.667) §
Spec	0.818±0.224(0.892)	0.794±0.308(0.889)	0.845±0.114(0.861)	0.840±0.124(0.838)	0.890±0.120 (0.917)	0.818±0.148(0.818)	0.873±0.172(0.955)	0.800±0.218(0.818)	0.882±0.122(0.909)	0.782±0.232 (0.818)
F1	0.564±0.201(0.563)	0.389±0.223(0.353) *	0.542±0.215(0.511)	0.616±0.217(0.544)	0.664±0.231 (0.628) */§	0.523±0.220(0.523)	0.236±0.234(0.234) **	0.399±0.208(0.500)	0.563±0.211(0.606)	0.609±0.143 (0.606)

AD: Alzheimer’s disease, MCI: mild cognitive impairment, Acc: accuracy, AUC: area under ROC curve, sens: sensitivity, spec: specificity, MD: mean diffusivity, MO: mode of anisotropy, FA: fractional anisotropy, GM: gray-matter volume, numbers are displayed as mean±standard deviation (median)

* Statistically significant from the corresponding MD measures using the Sign test p≤0.1

** Statistically significant from the corresponding MD measures using the Sign test p≤0.05

*** Statistically significant from the corresponding MD measures using the Sign test p≤0.01

‡ Statistically significant from the corresponding portions measures using the Sign test p≤0.1

‡‡Statistically significant from the corresponding portions measures using the Sign test p≤0.05

§ Statistically significant from the corresponding GM measures using the Sign test p≤0.1

Table 3

Classification results of the stacked diffusion maps.

	Portions mixed	Portions segregated	Annual mixed	Annual segregated
HC/ AD
Acc	0.955±0.040 (0.965)	0.786±0.108 (0.776) **	0.886±0.103 (0.939)	0.814±0.114 (0.818)
AUC	0.988±0.017 (0.992)	0.864±0.146 (0.904) **/§	0.964±0.054 (0.985)	0.876±0.119 (0.876) *
Sensitivity	0.897±0.102 (0.905)	0.663±0.196 (0.618) *	0.757±0.299 (0.833)	0.663±0.223 (0.667) §
Specificity	0.989±0.023 (1.000)	0.856±0.109 (0.861) ***	0.955±0.064 (1.000)	0.891±0.112 (0.909)
F1	0.934±0.061 (0.950)	0.688±0.170 (0.699) **	0.775±0.291 (0.909)	0.700±0.182 (0.641) §
HC/ MCI
Acc	0.930±0.033 (0.929)	0.708±0.104 (0.714) ***	0.870±0.046 (0.879)	0.662±0.097 (0.676) ***
AUC	0.991±0.007 (0.989)	0.738±0.146 (0.762) ***	0.957±0.063 (0.976)	0.753±0.091 (0.742) ***
Sensitivity	0.842±0.096 (0.800)	0.415±0.288 (0.300) **	0.660±0.138 (0.667)	0.360±0.238 (0.333) ***
Specificity	0.978±0.053 (1.000)	0.873±0.170 (0.946) **	0.982±0.038 (1.000)	0.827±0.163 (0.864) ***
F1	0.895±0.050 (0.889)	0.466±0.208 (0.445) **	0.773±0.093 (0.775)	0.398±0.201 (0.437) ***

AD: Alzheimer’s disease, MCI: mild cognitive impairment, Acc: accuracy, AUC: area under ROC curve, sens: sensitivity, spec: specificity, MD: mean diffusivity, MO: mode of anisotropy, FA: fractional anisotropy, GM: gray-matter volume, numbers are displayed as mean±standard deviation (median)

* Statistically significant from the corresponding mixed measures using the Sign test p≤0.1

** Statistically significant from the corresponding mixed measures using the Sign test p≤0.05

*** Statistically significant from the corresponding mixed measures using the Sign test p≤0.01

§ Statistically significant from the corresponding MD measures using the Sign test p≤0.1

Further, incorporating the GM with the MD (cascading them as deeper volume denoted by MD+GM) improved the results (Table 3), sometimes significantly depending on the performance measure involved, compared with either MD or GM alone. Specifically, MD+GM produced an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, a specificity of 93.9% and 89% and an AUC of 0.94 and 0.84 for AD and MCI classification respectively. Cascading the three maps resulted in the least performance (Table 3); MD+MO+FA produced an accuracy of 78.6% and 70.8%, a sensitivity of 66.3% and 41.5%, a specificity of 85.6% and 87.3% and an AUC of 0.86 and 0.74 for the classification of AD and MCI respectively versus HC.

Analysis while including a single scan per year for the same subject

Generally speaking, excluding the scans that belonged to the same subject that were carried out within less than a year resulted in an insignificant drop in the performance in terms of accuracy, AUC, sensitivity, specificity, and F1-score, as shown in Tables 2 and 3.

Analysis of segregated versus mixed training and test datasets

Mixing up the scans for one subject in both the training and test sets in one cross-validation yielded overfitting; in particular, the results were generally statistically significantly higher in mixed portions experiments with respect to segregated ones. The level of significance was less when the input scans were removed if two scans for the same subject were performed in less than a year (Table 3). The accuracy and AUC for the cascaded mixed maps were 16.9% and 0.12 respectively higher than the corresponding segregated ones for HC/AD classification and 22.2% and 0.25 respectively for HC/MCI classification; this highlights the overfitting severity encountered.

The ROC curves for all analyses are displayed in Fig 3, and summary of results is tabulated in Table 4.

Fig 3

ROC curves of left: AD/HC classification, right: MCI/HC classification.

Table 4

Summary of results.

Study	Study sample	Methodology	Results	Data type(s) (Dataset)
Liu et al., 2018 [58]	199 AD and 229 HC	CNN on landmarks learnt by statistical significance tests	Accuracy and AUC: HC/AD: 90.56% and 0.96	MRI (ADNI)
Lin et al., 2018 [59]	188 AD, 229 HC, 169 MCIc, and 193 MCInc	CNNs	Accuracy and AUC:HC/AD: 88.79%MCIc/MCInc: 79.9% and 0.86	MRI (ADNI)
Islam et al., 2018 [60]	316 non-demented, 70 very-mild, 28 mild, and 2 with moderate dementia	Ensemble of very deep (~120–169 convolutional layers) CNNs	Average multilabel classification accuracy of 93.18%	MRI (OASIS [61])
Wen et al., 2018 [62]	46 AD and 46 HC	Linear SVM for MD and FA whole maps only	Accuracy and AUC: MD-GM 76%, 0.83FA-GM 71%, 0.77	MRI and DTI (ADNI)
Khvostikov et al., 2018 [63]	48 AD, 108 MCI, and 58 HC	MD only, only hippocampus, CNN, with/without augmentation	Accuracy: MRI, MDHC/AD: 85%, 97%HC/MCI: 66%, 63%MCI/AD: 75%, 80%	MRI and DTI (ADNI)
Ahmed et al., 2017 [14]	45 AD, 58 MCI, and 52 HC	MD, MRI, hippocampus bounding box, multiple kernel learning	Accuracy: HC/AD: 90.2%HC/MCI: 79.42%MCI/AD: 76.63%	MRI, DTI, and CSF (ADNI)
Nir et al., 2015 [64]	37 AD, 113 MCI, and 50 HC	Linear SVM for MD and FA maximum path density (MPD) maps (MD performed better)	Accuracy:HC/AD: 74.5–84.9%HC/MCI: 68.3–79% (MD only)	DTI (ADNI)
Lee et al., 2015 [65]	22 AD, 47 MCI, and 22 HC	SVM on FA and MO from TBSS	Accuracy:HC/AD: FA 90.9%, MO 97.7MCI/AD: FA 88.4%, MO 98.6	DTI (ADNI)
Dyrba et al, 2012 [17]	137 AD and 143 HC	Multi-kernel SVM, MD, FA, WM, GM	Accuracy:HC/AD: MD 83.3%FA 80.3%GM 89.3%MD+GM 88.7%MD+GM+FA 89.1%	MRI and DTI (EDSD)
The proposed algorithm	115 AD, 106 MCI, and 185 HC	Small CNN, MD, FA, MO, GM	Accuracy and AUC:HC/AD: MD 88.9%, 0.93FA 86%, 0.88MO 82.8%, 0.88GM 91.3%, 0.96MD+GM 93.5%, 0.94HC/ MCI: MD 71.1%, 0.68FA 72.1%, 0.73MO 64.4%, 0.62GM 75.7%, 0.80MD+GM 79.6%, 0.84	MRI and DTI (ADNI)

AD: Alzheimer’s disease, MCI: mild cognitive impairment, MCIc: MCI convert, MCInc: MCI non convert, HC: healthy controls, MD: mean diffusivity, MO: mode of anisotropy, FA: fractional anisotropy, GM: gray matter, WM: white matter, CSF: cerebrospinal fluid, TBSS: tract-based spatial statistics, SVM: support vector machine, CNN: convolutional neural network, AUC: area under curve, MRI: magnetic resonance imaging, DTI: diffusion tensor imaging, ADNI: Alzheimer’s disease neuroimaging initiative, OASIS: open access series of imaging studies, EDSD: European DTI study on dementia.

Discussion

MD outperformed both MO and FA, when employing the individual maps for the classification task with AUC of 0.93, 0.88 and 0.88 for MD, FA, and MO respectively, and this seems to be in concordance with the results from [17,26,29,64]. Douaud et al. [26] reported out that the significant variations in MD, as opposed to FA and MO, primarily in the amygdala-hippocampus complex. Kantarci et al. [29]found out that the MD in the hippocampal and para-hippocampal areas was complementary to the GM volume for the classification of HC/AD. Firbank et al. [66] added that the clusters where MD was significantly higher in AD subjects than that of controls were primarily in the left temporal lobe; that was parallel with atrophy in the grey matter in these locations. Further, Rose et al. [67] reported that MD was elevated significantly at the hippocampus, amygdala, and entorhinal cortex, whereas, FA was reduced significantly mainly at the thalamus. Also, they showed that the cortical areas with increased MD correlate with regions of reduced gray matter density measured using structural MRI in patients with AD. It is worthy pointing out that the results in this work, coincide with [67].

In this work, the FA yielded better results than MO. This seems to be in contrast to the low-sample study of [65] where the MO yielded accuracy that was ~7%-10% higher than that driven by FA in HC/AD and HC/MCI respectively. It is worth noting that the sample size, used in this study, was at least five-fold that of Lee et al [65]. The cascaded diffusion maps yielded worse performance, but not always significantly, than employing the MD.

The GM volumes alone, in agreement with the literature, improved the results [17,68]; this is attributed to the fact that AD is prominently characterized by amyloid plaques and neurofibrillary tangles that deposit in the GM which, in turn, leads to the death of the neurons and the thinning of the cortex or simply atrophy [69-72]. Oishi et al. reported in their study “DTI is useful for localizing and quantifying the anatomical abnormalities, but apparently not adequate to investigate the histopathological background of the diseases” [71]. They explained that the DTI measures could be affected by the pathology or other reasons. For example, the diffusion lasts for up to 100 ms in a radius of up to 10 μm that is to be averaged over a voxel of 2–3 mm in size; this indeed makes it more sensitive to the presence of multiple fiber bundles and partial volume effect [71,73]. In addition, in Henf et al.’s work, they concluded that without applying the partial volume correction, MD was not superior to gray matter volume in separating MCI and AD from HC [73].

It is important to assert that in this work, the volumes namely; MD, FA, and MO, and GM and MD were cascaded. Whereas, Wen et al. [62] assessed the MD and FA values over the GM mask (Table 4), and therefore, the performance of the two works cannot be properly compared.

Dyrba et al. [17], using the European DTI study on dementia (EDSD) cohort, reported that combining the MD with GM extracted from structural MRI, where Support Vector Machine (SVM) was utilized, had worsened the results of GM alone. Moreover, the authors reported that the GM utilization outperformed the MD in terms of accuracy, sensitivity and specificity (Table 4). In this work, incorporating the GM with the MD improved the results; not always significantly though.

One of the biggest hurdles encountered when dealing with machine learning in general, and neural networks in specific, is the limitation of the dataset; especially when dealing with medical data; this is the main cause of overfitting [74]. The batch normalization layer is used to reduce the problem of overfitting [75,76] due to its importance in deep learning [77]. In addition, the usage of small-sized filters is usually enhancing the test set performance measures compared to larger filters as explained in the Methods section, through decreasing the overfitting which aligns with Pereira et al. [78] who advocated that small filter sizes of 3×3 would minimize the effect of overfitting since the number of parameters to be learnt decreased. Further, Simonyan and Zisserman [52] explained that the effective receptive field of two stacked 3×3 convolutional layers was equivalent to a single 5×5 layer and that of three stacked 3×3 convolutional layers was equivalent to a single 7×7 layer. Moreover, increasing the number of layers increases nonlinearities, which also decreases the weights, to be optimized, by 77% and 81% for the first and the second case respectively. In addition, the proposed architecture comprised of only one or two layers in depth to alleviate the problem of overfitting; this is in agreement with Ahmed et al. [76] and RStudio online tutorials [79]. Though, ten-fold cross-validation technique was incorporated to give a good estimate about the generalizability of the classification[80,81].

It can be noticed that the drop of the performance between portions (all scans of the subject are included) and annual (only scans a year or more apart are included) was minor; this could be attributed to the fact that the number of the scans, upon being annually-scanned, dropped -at least- to half of those without this constraint (Table 1).

As shown previously in the Results section, the effect of segregating the scans of the same subject to either the learning or the testing data versus randomly selecting the scans with no constraints during the cross-validation folds that the accuracy and AUC dropped by around 17% and 0.124 respectively at p<0.05 for the HC/AD classification and 22.2% and 0.25 respectively at p<0.01 for HC/MCI classification.

This could be interpreted as an overfitting case where during the cross-validation pass, the network considered the temporal instance of the scan of the same subject as a previously seen scan in the training stage; where there is a spatial dependency in the same subject as the disease progresses. This overfitting case would promote the classification performance task. [44,82-84].

Further, the average execution time for the entire ten-fold cross-validation, training and testing, was 12.5 minutes, and the average time per one scan during testing was 0.005 seconds; this is quite competitive when the availability of a graphical processing unit (GPU) is restricted or not possible.

It is important to highlight that all models, proposed in this study, had their specificity higher than their sensitivity; i.e. they are better at handling true negatives than true positives. Coherent to this, some analyses suggested the presence of a trade-off between these two measures[85-87]. This is mainly due to the fact that number of healthy subjects used in this study was quite larger than the number of MCI and AD subjects [85,86].

It is worthy to mention that incorporating the CSF amyloid data could be considered to be interpreted and asses its role in differentiating cognitive deficits. Longitudinal assessment of the cases should be studied; this is a promising means of early detection of the onset of AD which helps aid AD drug discovery and testing.

Conclusion

In this paper, a CNN was handcrafted to classify MCI and AD from HC. The MD, FA, MO, GM, MD+GM scans were compared; MD was the best-performing diffusion map amongst the diffusion maps regarding classification in terms of accuracy, specificity, and AUC of 88.9%, 91.7% and 0.93 respectively for HC/AD classification, and 71.1%, 81.8% and 0.68 respectively for HC/MCI classification. Combining GM with MD enhanced the performance but below the 5% significance level; to give an accuracy, a specificity, and an AUC of 93.5%, 93.9% and 0.94 respectively for HC/AD classification and 79.6%, 89% and 0.84 respectively for HC/MCI classification.

The dataset comprised more than one instance per subject and in this work, it is recommended that the training and test sets should be split such that one’s scans were in the same pile; i.e. the IDs of the subjects in the training set and the test set should not overlap.

This file contains a list of the ethics committees/institutional review boards that approved the ADNI study.

(PDF)

Click here for additional data file.

14 Feb 2020

PONE-D-19-35687

Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

PLOS ONE

Dear Dr. Marzban,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration by two Reviewers and an Academic Editor, please make the suggested corrections posed by both Reviewers so I can render a decision on this manuscript.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The aim of this work is to provide an automatic classification of the MCI and AD versus HC.

For some subjects having multiple scans at different timepoints, the effect of selecting only scans that were taken a year or more from the previous one with respect to the same subject has been investigated.

In addition, the impact of having different timepoint scans for the same subject in the training set and how the separation based on the subject has been assessed in terms of the effect on the overall performance.

The topic handled in this study is important and an active research area.

The paper has been well-organized.

However, the following revisions are required to improve its quality.

1) Optimization is an important stage and the standard (Euclidean) gradient descent optimization has been used in this work.

The authors should add the following statement to make clear that this approach presents efficient results, so it has been applied:

"Recently, Sobolev gradient based optimization has been used in deep network based methods to diagnose AD [R1,R2]. However, the standard gradient descent optimization is efficient in the proposed approach in terms of computation."

R1:"Diagnosis of Alzheimer's Disease with Sobolev Gradient Based Optimization and 3D Convolutional Neural Network", Numerical Methods in Biomedical Engineering, 35(7),2019

R2:"Fully Automated Classification of Brain Tumors Using Capsules for Alzheimer’s Disease Diagnosis", IET Image Processing, 10.1049/iet-ipr.2019.0312, 2019

2) The following sentence should be updated to make its meaning clearer:

"In comparison with the diffusion maps in comparison, MD outperformed the other two maps; namely: FA and MO."

3) There are many different approaches in this area.

The reason to use a deep learning based technique in this study should be explained clearly by supporting appropriate recent studies.

Therefore, the following statements can be added:

"Image based diagnosis of AD is important and required mainly to avoid subjective assessments [R1]. Deep learning based methods gives successful results particularly in medical image analysis [R2] due to flexible and efficient formulations [R3]."

R1: "Biomedical Information Technology: Image Based Computer Aided Diagnosis Systems", The 7th Int.Conf. on Advanced Technologies, 2018

R2: "Deep Learning in Medical Image Analysis: Recent Advances and Future Trends", Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2017 (CGVCVIP 2017), Lisbon, Portugal

R3:"Formulas Behind Deep Learning Success",Int.Conf. on Applied Analysis and Mathematical Modeling (ICAAMM2018), 2018

4) There is typo in; "Moreover, Increasing the number ....", which should be "Moreover, increasing the number...."

5) The meaning of the sentence;

"....the limitation of the dataset; especially when dealing with medical data; this is the main cause of overfitting."

should be supported with the following recent work indicating main issues in deep learning based methods:

"Challenges and Recent Solutions for Image Segmentation in the Era of Deep Learning", 9th Int. Conf. on Image Processing Theory, Tools and Applications (IPTA),2019

The following sentence;

"The batch normalization layer is used to reduce the problem of overfitting [68,69]"

should be updated as

"The batch normalization layer is used to reduce the problem of overfitting [68,69] due to the importance of it in deep learning [R]"

R:"On The Importance of Batch Size for Deep Learning", Int. Conf. on Mathematics (ICOMATH2018), An Istanbul Meeting for World Mathematicians, Istanbul, Turkey,2018

Reviewer #2: The authors compared different input data settings and experiments settings to predict AD/MCI/HC from DTI using CNNs. The experiments and analyses are comprehensive with in-depth discussions. The paper is well presented and has enough novelty. The only improvement I can think of is that the author weren't super clear about the CNNs' structures. It is unclear it is a 2D or a 3D CNN. In addition, the author didn't mention how multiple input were handled when there were more than one input to the CNN, e.g. MD+GM. And the cascading method is also not clearly defined. Lacking these details might lead to difficulty in reproducing the work. I suggest the author add those technical details to the method section.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

We would appreciate receiving your revised manuscript by June, 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for including your ethics statement:

This study was carried out in accordance with the recommendations of the Declaration of Helsinki. The ADNI protocol was approved by the Institutional Review Boards of all of the participating institutions. Informed written consent was obtained from all participants at each site.

Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.

Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

4. Thank you for stating the following in the Acknowledgments Section of your manuscript:

"Data collection and sharing for this project was funded by the

Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant

U01 AG024904). The ADNI was launched in 2003 by the National Institute on Aging (NIA),

the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and

Drug Administration (FDA), private pharmaceutical companies and non-profit organizations,

as a $60 million, 5-year public-private partnership. ADNI is funded by the National Institute

on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through

generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug

Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers

Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and

Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech,

Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research &

Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.;

Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research;

Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal

Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The

Canadian Institute of Health Research is providing funds to support ADNI clinical sites in

Canada. Private sector contributions are facilitated by the Foundation for the National

Institutes of Health (www.fnih.org). The grantee organization is the Northern California

Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease

Cooperative Study at the University of California, San Diego. ADNI data are disseminated by

the Laboratory for Neuroimaging at the University of Southern California."

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

"The authors received no specific funding for this work."

Additionally, because some of your funding information pertains to commercial funding, we ask you to provide an updated Competing Interests statement, declaring all sources of commercial funding.

In your Competing Interests statement, please confirm that your commercial funding does not alter your adherence to PLOS ONE Editorial policies and criteria by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” as detailed online in our guide for authors  http://journals.plos.org/plosone/s/competing-interests.  If this statement is not true and your adherence to PLOS policies on sharing data and materials is altered, please explain how.

Please include the updated Competing Interests Statement and Funding Statement in your cover letter. We will change the online submission form on your behalf.

26 Feb 2020

Dear Dr. Ginsberg,

We would like to submit the revised manuscript entitled “Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks” for reconsideration by the journal of PLOS ONE.

We have responded carefully to the questions and concerns raised by the reviewers, where our responses and actions items are inline in blue and preceded by ampersand symbol (&).

Please address all correspondence concerning this manuscript to me at eman.marzban@eng1.cu.edu.eg

Thank you for your consideration of this manuscript.

Sincerely,

Eman N. Marzban

Faculty of Engineering, Cairo University, Cairo Uni. str., 12613, Giza, Egypt

Tel.: +2-0235703426, +2-01003320868

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

& We updated the cover letter and appended this sentence to it, paragraph no. 3, line 20:

“Data availability statement: The data set is owned by a third-party organization; the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data are publicly and freely available from the http://adni.loni.usc.edu/data-samples/access-data/ Institutional Data Access / Ethics Committee (contact via http://adni.loni.usc.edu/data-samples/access-data/) upon sending a request that includes the proposed analysis and the named lead investigator.”________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The aim of this work is to provide an automatic classification of the MCI and AD versus HC.

For some subjects having multiple scans at different timepoints, the effect of selecting only scans that were taken a year or more from the previous one with respect to the same subject has been investigated.

In addition, the impact of having different timepoint scans for the same subject in the training set and how the separation based on the subject has been assessed in terms of the effect on the overall performance.

The topic handled in this study is important and an active research area.

The paper has been well-organized.

However, the following revisions are required to improve its quality.

1) Optimization is an important stage and the standard (Euclidean) gradient descent optimization has been used in this work.

The authors should add the following statement to make clear that this approach presents efficient results, so it has been applied:

"Recently, Sobolev gradient based optimization has been used in deep network based methods to diagnose AD [R1,R2]. However, the standard gradient descent optimization is efficient in the proposed approach in terms of computation."

R1:"Diagnosis of Alzheimer's Disease with Sobolev Gradient Based Optimization and 3D Convolutional Neural Network", Numerical Methods in Biomedical Engineering, 35(7),2019

R2:"Fully Automated Classification of Brain Tumors Using Capsules for Alzheimer’s Disease Diagnosis", IET Image Processing, 10.1049/iet-ipr.2019.0312, 2019

& We would like to thank the reviewer for his valuable suggestions.

The references were added as suggested as references No. 47 and no. 48, in page 31. The added references are cited in line 122, page 8.

2) The following sentence should be updated to make its meaning clearer:

"In comparison with the diffusion maps in comparison, MD outperformed the other two maps; namely: FA and MO."

& The sentence was changed to be “MD outperformed both MO and FA, when employing the individual maps for the classification task with AUC of 0.93, 0.88 and 0.88 for MD, FA, and MO respectively.” in line 286, page 21.

3) There are many different approaches in this area.

The reason to use a deep learning based technique in this study should be explained clearly by supporting appropriate recent studies.

Therefore, the following statements can be added:

"Image based diagnosis of AD is important and required mainly to avoid subjective assessments [R1]. Deep learning based methods gives successful results particularly in medical image analysis [R2] due to flexible and efficient formulations [R3]."

R1: "Biomedical Information Technology: Image Based Computer Aided Diagnosis Systems", The 7th Int.Conf. on Advanced Technologies, 2018

R2: "Deep Learning in Medical Image Analysis: Recent Advances and Future Trends", Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2017 (CGVCVIP 2017), Lisbon, Portugal

R3:"Formulas Behind Deep Learning Success",Int.Conf. on Applied Analysis and Mathematical Modeling (ICAAMM2018), 2018

& The references were added as suggested, as references No. 4, 5, and 6, in page 28. These references are cited in lines 48 and 49, page 4.

4) There is typo in; "Moreover, Increasing the number ....", which should be "Moreover, increasing the number...."

& The typo has been updated to " Moreover, increasing the number of layers increases nonlinearities,…” in line 337 page 23.

5) The meaning of the sentence;

"....the limitation of the dataset; especially when dealing with medical data; this is the main cause of overfitting."

should be supported with the following recent work indicating main issues in deep learning based methods:

"Challenges and Recent Solutions for Image Segmentation in the Era of Deep Learning", 9th Int. Conf. on Image Processing Theory, Tools and Applications (IPTA),2019

& The reference was added as suggested, as reference No. 74 in page 33. The reference is cited in line 329 page 22.

The following sentence;

"The batch normalization layer is used to reduce the problem of overfitting [68,69]"

should be updated as

"The batch normalization layer is used to reduce the problem of overfitting [68,69] due to the importance of it in deep learning [R]"

R:"On The Importance of Batch Size for Deep Learning", Int. Conf. on Mathematics (ICOMATH2018), An Istanbul Meeting for World Mathematicians, Istanbul, Turkey,2018

& The reference was added as suggested, as reference No 77 in page 33. The reference is cited in line 330 in page 22.

Reviewer #2: The authors compared different input data settings and experiments settings to predict AD/MCI/HC from DTI using CNNs. The experiments and analyses are comprehensive with in-depth discussions. The paper is well presented and has enough novelty. The only improvement I can think of is that the author weren't super clear about the CNNs' structures. It is unclear it is a 2D or a 3D CNN. In addition, the author didn't mention how multiple input were handled when there were more than one input to the CNN, e.g. MD+GM. And the cascading method is also not clearly defined. Lacking these details might lead to difficulty in reproducing the work. I suggest the author add those technical details to the method section.

& We would like to thank the reviewer for the valuable comment and inquiries. We have updated the description of the network, in several places, to include all missing information. The updated paragraphs are listed below:

1- Paragraph No. 1, starting at line 114 in page 8:

"To address the classification task, a 2D CNN was employed, having the advantage of taking into consideration the spatial relationships between pixels; especially with a pathology that would progress over time and brain regions [19,42-44]."

2- Paragraph No 2, line 146 in page 9:

"Cascading in this study was done by concatenating the diffusion map volumes following each other in depth. Thus, the original size of 61×37×38 for one map would increase to 61×37×76 and 61×37×114 in case of two and three maps respectively; where the third dimension is normal to the axial plane as described in Fig 2."

3- Paragraph No 2, line 211 in page 11:

"Upon evaluating the different hyperparameters of 2D CNNs, the optimal CNN size, for one volume (MD, MO, FA, or GM) each of which is 61×37×38, is formed of one layer in depth having five filters each of which was 5×5×38. On the other hand, the optimal CNN size for cascaded volumes experiment; namely MD+MO+FA of size 61×37×114 and GM+MD of size 61×37×76, is formed of two layers; where the first layer included thirty 1×1×114 or thirty 1×1×76 filters respectively and the second layer included five 3×3×30 filters. Regarding 2D CNNs, it is worth pointing out that the depth of the filters must match with that of the input volumes, and that the depth of the output of the convolution must match with the number of filters [57]."________________________________________

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

& All figures were confirmed to comply with your standards via figures@plos.org

We would appreciate receiving your revised manuscript by June, 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

& Thank you for your valuable comment. The manuscript has been updated to follow the PLOS ONE's style requirements.

2. Thank you for including your ethics statement:

This study was carried out in accordance with the recommendations of the Declaration of Helsinki. The ADNI protocol was approved by the Institutional Review Boards of all of the participating institutions. Informed written consent was obtained from all participants at each site.

Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.

Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

& Thank you very much for your review. It is important to highlight that we did not implement the data acquisition ourselves; our study was carried out on the publicly and freely available data from the ADNI repository. Where the process is usually started by submitting a research protocol to ADNI. Once the proposal is protocol; ADNI would grant the research team the data access, for download, through their website (http://adni.loni.usc.edu/data-samples/access-data/ ).

It is important to note that the interaction with human beings, of the ADNI data acquisition team, for scanning and diagnosis was conformant with the declaration of Helsinki.

To mitigate the Journal requirements, we updated the cover letter and appended the following sentence, which would clarify that we employed the data only for the analysis but our study did not employ any patient interaction or data acquisition in Different places as listed below:

1- The revised cover letter, paragraph no. 1, page 2, line 25

“Further, please find more details about the ADNI project and data acquisition and sharing policies and protocol:

Data sharing policy and data access process: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_DSP_Policy.pdf

ADNI steering committee and list of acknowledgment for publications using ADNI repository: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

ADNI protocol and ethics statement: http://adni.loni.usc.edu/wp-content/themes/freshnews-dev-v2/documents/clinical/ADNI-2_Protocol.pdf”

2- In the revised cover letter, page 1, paragraph 3, line 20

"Data availability statement: The dataset, employed in this study, is owned by a third-party organization; the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data are publicly and freely available from the http://adni.loni.usc.edu/data-samples/access-data/ Institutional Data Access / Ethics Committee (contact via http://adni.loni.usc.edu/data-samples/access-data/) upon sending a request that includes the proposed analysis and the named lead investigator."

3- In the ethics statement in the online submission, we included:

“The proposed algorithm uses the ADNI data repository. All ADNI studies are conducted according to the Good Clinical Practice guidelines, the Declaration of Helsinki, and U.S. 21 CFR Part 50 (Protection of Human Subjects), and Part 56 (Institutional Review Boards). Written informed consent was obtained from all participants before protocol-specific procedures were performed. The ADNI protocol was approved by the Institutional Review Boards of all of the participating institutions. Ethics committees/institutional review boards that approved the study are:

Albany Medical Center Committee on Research Involving Human Subjects Institutional Review Board, Boston University Medical Campus and Boston Medical Center Institutional Review Board, Butler Hospital Institutional Review Board, Cleveland Clinic Institutional Review Board, Columbia University Medical Center Institutional Review Board, Duke University Health System Institutional Review Board, Emory Institutional Review Board, Georgetown University Institutional Review Board, Health Sciences Institutional Review Board, Houston Methodist Institutional Review Board, Howard University Office of Regulatory Research Compliance, Icahn School of Medicine at Mount Sinai Program for the Protection of Human Subjects, Indiana University Institutional Review Board, Institutional Review Board of Baylor College of Medicine, Jewish General Hospital Research Ethics Board, Johns Hopkins Medicine Institutional Review Board, Lifespan - Rhode Island Hospital Institutional Review Board, Mayo Clinic Institutional Review Board, Mount Sinai Medical Center Institutional Review Board, Nathan Kline Institute for Psychiatric Research & Rockland Psychiatric Center Institutional Review Board, New York University Langone Medical Center School of Medicine Institutional Review Board, Northwestern University Institutional Review Board, Oregon Health and Science University Institutional Review Board, Partners Human Research Committee Research Ethics, Board Sunnybrook Health Sciences Centre, Roper St. Francis Healthcare Institutional Review Board, Rush University Medical Center Institutional Review Board, St. Joseph's Phoenix Institutional Review Board, Stanford Institutional Review Board, The Ohio State University Institutional Review Board, University Hospitals Cleveland Medical Center Institutional Review Board, University of Alabama Office of the IRB, University of British Columbia Research Ethics Board, University of California Davis Institutional Review Board Administration, University of California Los Angeles Office of the Human Research Protection Program, University of California San Diego Human Research Protections Program, University of California San Francisco Human Research Protection Program, University of Iowa Institutional Review Board, University of Kansas Medical Center Human Subjects Committee, University of Kentucky Medical Institutional Review Board, University of Michigan Medical School Institutional Review Board, University of Pennsylvania Institutional Review Board, University of Pittsburgh Institutional Review Board, University of Rochester Research Subjects Review Board, University of South Florida Institutional Review Board, University of Southern, California Institutional Review Board, UT Southwestern Institution Review Board, VA Long Beach Healthcare System Institutional Review Board, Vanderbilt University Medical Center Institutional Review Board, Wake Forest School of Medicine Institutional Review Board, Washington University School of Medicine Institutional Review Board, Western Institutional Review Board, Western University Health Sciences Research Ethics Board, and Yale University Institutional Review Board.”

4- And in the Methods section, paragraph No 1, page 5, line 77:

"…All ADNI studies are conducted according to the Good Clinical Practice guidelines, the Declaration of Helsinki, and U.S. 21 CFR Part 50 (Protection of Human Subjects), and Part 56 (Institutional Review Boards). Written informed consent was obtained from all participants before protocol-specific procedures were performed.”

5- And in the footnote of Methods section, page 6:

“Ethics committees/institutional review boards that approved the ADNI study are: Albany Medical Center Committee on Research Involving Human Subjects Institutional Review Board, Boston University Medical Campus and Boston Medical Center Institutional Review Board, Butler Hospital Institutional Review Board, Cleveland Clinic Institutional Review Board, Columbia University Medical Center Institutional Review Board, Duke University Health System Institutional Review Board, Emory Institutional Review Board, Georgetown University Institutional Review Board, Health Sciences Institutional Review Board, Houston Methodist Institutional Review Board, Howard University Office of Regulatory Research Compliance, Icahn School of Medicine at Mount Sinai Program for the Protection of Human Subjects, Indiana University Institutional Review Board, Institutional Review Board of Baylor College of Medicine, Jewish General Hospital Research Ethics Board, Johns Hopkins Medicine Institutional Review Board, Lifespan - Rhode Island Hospital Institutional Review Board, Mayo Clinic Institutional Review Board, Mount Sinai Medical Center Institutional Review Board, Nathan Kline Institute for Psychiatric Research & Rockland Psychiatric Center Institutional Review Board, New York University Langone Medical Center School of Medicine Institutional Review Board, Northwestern University Institutional Review Board, Oregon Health and Science University Institutional Review Board, Partners Human Research Committee Research Ethics, Board Sunnybrook Health Sciences Centre, Roper St. Francis Healthcare Institutional Review Board, Rush University Medical Center Institutional Review Board, St. Joseph's Phoenix Institutional Review Board, Stanford Institutional Review Board, The Ohio State University Institutional Review Board, University Hospitals Cleveland Medical Center Institutional Review Board, University of Alabama Office of the IRB, University of British Columbia Research Ethics Board, University of California Davis Institutional Review Board Administration, University of California Los Angeles Office of the Human Research Protection Program, University of California San Diego Human Research Protections Program, University of California San Francisco Human Research Protection Program, University of Iowa Institutional Review Board, University of Kansas Medical Center Human Subjects Committee, University of Kentucky Medical Institutional Review Board, University of Michigan Medical School Institutional Review Board, University of Pennsylvania Institutional Review Board, University of Pittsburgh Institutional Review Board, University of Rochester Research Subjects Review Board, University of South Florida Institutional Review Board, University of Southern, California Institutional Review Board, UT Southwestern Institution Review Board, VA Long Beach Healthcare System Institutional Review Board, Vanderbilt University Medical Center Institutional Review Board, Wake Forest School of Medicine Institutional Review Board, Washington University School of Medicine Institutional Review Board, Western Institutional Review Board, Western University Health Sciences Research Ethics Board, and Yale University Institutional Review Board.”

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

& Thank you for your comment. We updated the cover letter, where the following sentence is appended in page 1, paragraph no. 3, line 20:

“Data availability statement: The data set, employed in this study, is owned by a third-party organization; the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data are publicly and freely available from the http://adni.loni.usc.edu/data-samples/access-data/ Institutional Data Access / Ethics Committee (contact via http://adni.loni.usc.edu/data-samples/access-data/) upon sending a request that includes the proposed analysis and the named lead investigator.”

4. Thank you for stating the following in the Acknowledgments Section of your manuscript:

"Data collection and sharing for this project was funded by the

Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant

U01 AG024904). The ADNI was launched in 2003 by the National Institute on Aging (NIA),

the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and

Drug Administration (FDA), private pharmaceutical companies and non-profit organizations,

as a $60 million, 5-year public-private partnership. ADNI is funded by the National Institute

on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through

generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug

Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers

Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and

Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech,

Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research &

Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.;

Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research;

Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal

Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The

Canadian Institute of Health Research is providing funds to support ADNI clinical sites in

Canada. Private sector contributions are facilitated by the Foundation for the National

Institutes of Health (www.fnih.org). The grantee organization is the Northern California

Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease

Cooperative Study at the University of California, San Diego. ADNI data are disseminated by

the Laboratory for Neuroimaging at the University of Southern California."

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

"The authors received no specific funding for this work."

Additionally, because some of your funding information pertains to commercial funding, we ask you to provide an updated Competing Interests statement, declaring all sources of commercial funding.

In your Competing Interests statement, please confirm that your commercial funding does not alter your adherence to PLOS ONE Editorial policies and criteria by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests. If this statement is not true and your adherence to PLOS policies on sharing data and materials is altered, please explain how.

Please include the updated Competing Interests Statement and Funding Statement in your cover letter. We will change the online submission form on your behalf.

& Indeed, we did not receive any fund (neither from research entities nor from commercial ones) in the very strict sense, and we did not implement data acquisition on our own. Nevertheless, we employed the ADNI dataset in our study. It is important to note that it is mandatory to acknowledge the ADNI data repository as per their data sharing policy (the link is listed at the end of the paragraph). ADNI gets the aforementioned fund as an entirely different entity than ours. ADNI is currently an ongoing project where entity itself is funded to perform the data acquisition including the scans, diagnosis, cognitive assessment, other medical maneuvers, and also to disseminate the data upon requests as explained in http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_DSP_Policy.pdf.

In order to mitigate the confusion, we added the paragraph below in the revised cover letter in page 2, paragraph no. 1, line 25:

“Further, please find more details about the ADNI project and data acquisition and sharing policies and protocol:

Data sharing policy and data access process: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_DSP_Policy.pdf

ADNI steering committee and list of acknowledgment for publications using ADNI repository: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

ADNI protocol and ethics statement: http://adni.loni.usc.edu/wp-content/themes/freshnews-dev-v2/documents/clinical/ADNI-2_Protocol.pdf

Funding Statement: The authors received no specific funding for this work.

Competing Interests Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.”

Submitted filename: Rebuttal letter.docx

Click here for additional data file.

2 Mar 2020

Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

PONE-D-19-35687R1

Dear Dr. Marzban,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

11 Mar 2020

PONE-D-19-35687R1

Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

Dear Dr. Marzban:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Stephen D. Ginsberg

Section Editor

PLOS ONE