Discovering the neural correlate informed nosological relation among multiple neuropsychiatric disorders through dual utilisation of diagnostic information
Authors: Wenjun Bai
Tomoki Tokuda
Okito Yamashita
Junichiro Yoshimoto
Venue: ICLR 2022 Submitted (2022)
Keywords: computational psychiatric
variational auto-encoder
fMRI analysis

Abstract: The unravelled nosological relation among diverse types of neuropsychiatric disorders serves as an important precursor in advocating the dimensional approach to psychiatric classification. Leveraging high-dimensional abnormal resting-state functional connectivity, the crux of mining corresponded nosological relations is to derive a low-dimensional embedding space that preserves the diagnostic attributes of represented disorders. To accomplish this goal, we seek to exploit the available diagnostic information in learning the optimal embedding space by proposing a novel type of conditional variational auto-encoder that incorporates dual utilisation of diagnostic information. Encouraged by the achieved promising results in challenging the conventional approaches in low dimensional density estimation of synthetic functional connectivity features, we further implement our approach on two empirical neuropsychiatric neuroimaging datasets and discover a reliable nosological relation among autism spectrum disorder, major depressive disorder, and schizophrenia.

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DISCOVERING THE NEURAL CORRELATE INFORMED NOSOLOGICAL RELATION AMONG MULTIPLE NEU- ROPSYCHIATRIC DISORDERS THROUGH DUAL UTILISA- TION OF DIAGNOSTIC INFORMATION
Anonymous authors Paper under double-blind review
ABSTRACT
The unravelled nosological relation among diverse types of neuropsychiatric dis- orders serves important precursor in advocating the dimensional approach to psychiatric classification. Leveraging nigh-dimensional abnormal resting-state functional connectivity, the crux of mining corresponded nosological relations is to derive a low-dimensional embedding space that preserves the diagnostic at- tributes of represented disorders. To accomplish this goal, we seek exploit the available diagnostic information in learning the optimal embedding space by proposing novel type of conditional variational auto-encoder that incorporates dual utilisation of diagnostic information. Encouraged by the achieved promising results challenging the conventional approaches low dimensional density es- timation of synthetic functional connectivity features, we further implement our approach on two empirical neuropsychiatric neuroimaging datasets and discover reliable nosological relation among autism spectrum disorder, major depressive disorder, and schizophrenia.
INTRODUCTION
1.1 NOSOLOGICAL RELATION AMONG NEUROPSYCHIATREE DISORDERS
Instead of the traditional discrete, categorical view on nosology of multiple neuropsychological disorders (Frances 2009), an alternative dimensional, continuous view, which suggests all mental illnesses lie along single low dimensional spectrum (Adam 2013; Casey et al. 2013 Helzer et al., 2009), uprises
a more promising perspective view the relation among diverse disorders. Despite its commonplace in the symptom-based clinical domain (Caspi & Moffit] 2018), it was not until recently that the finding shared genetic components across various neuropsychiatric disorders (Anttila et al. 2018) offered the first computational study to reveal the nosological relation among various disorders.
Aside from the discovered commonalities on the genetic makeup of various disorders, the resting- state functional connectivity (FC) has proved itself as a valuable biomarker in distinguishing disorder patients from healthy controls in plethora of computational studies (Woodward & Cascio 2015), they may also hold great promise for uncovering the complex, continuous relation among differ- neuropsychiatric disorders. Unfortunately, due their inherent high dimensionality, it poses a formidable data-analytic challenge in discovering the targeted nosological relation on the high- dimensional neuropsychiatric FC feature space. Thence, the crux of unveiling the FC informed nosological relation rests on finding an optimal low- wiwdemensiioal embedding space to be informative of high-dimensional FC feature space.
1.2 THE OBJECTIVE OF THIS RESEARCH
One straightforward approach is apply dimensionality reduction techniques to these high- dimensional FC features. However, as stated in Huys et al. (2016). without the supplied super- vision signals, over-reliance data-driven methods may not be sufficient to derive the optimal

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low-dimensional embeddings of FC features. Hence, the objective of this research is to seek the util- isation of supervision signals, e.g., diagnostic information, in learning a neural correlate informed low-dimensional embedding space, which allows to attain the nosological relation among diverse disorders.
To harness diagnostic information in learning targeted low-dimensional embedding space, in the following sections, we chiefly propose a novel type of conditional variational auto-encoder (VAE) incorporate dual utilisation of this information, which involves regularising the learning of em- beddings through the introduced implicit clustering effect, and encoding the diagnostic difference among disorders. Based on the demonstrated empirical superiority of our approach simulation study, we implemented it in mining a consistent nosological relation among three common disorders across two curated neuropsychiatric FC datasets.
In summarisation, main contributions of our work include:
putting forward a novel type of conditional VAF, which involves two utilisations of di- agnostic information in learning an optimal embedding space for high dimensional FC features;
mining consistent nosological relation among autism spectrum disorder, major depressive disorder, and schizophrenia across two curated neuropsychiatric FC datasets.
2 PREVIOUS WORKS
Hamessing the extracted brain features, recent efforts in computational psychiatry are largely ac- knowledged on developing increasingly complicated classification models to mine discriminative features to aid clinical diagnosis (Guo et 2017 Heinsfeld al,, 2018) and discovering the sub- types of single mental illness via advanced data-driven approaches, e.g., the multi-view clustering method (Tokuda al., 2018).
Unfortunately, little attention was paid to unveiling complex nosological relations among diverse types of neuropsychiatric disorders based on brain correlates. Among few attempts, worth men- tioned study that shares similar motivation with ours is the work of Xia et al. 2018), where di- mensions of psychopathology were closely tied to certain common brain features at the macro level, e.g., resting-state networks. However, this finding in support of the differentiation of disorders the brain feature level - reveals little on the nosological relationship among disorders, let alone their symptom-based methodology, which does fully harness the essence of brain features.
Narrowing our focus on prevailing methodological modifications on VAE, a plethora of variants ranging from the improved generalisation, e.g., VAF (Burgess et al. 2018). Vamp-VAF: (Tom- czak & Welling, 2018), the diversified generative process, e.g., EVAE (Bai et al. 2019). Multi-entity
VAE (Nash al. (017)). the optimised inference (encoding) process, c.g., Info- VAE (Zhao al.,
2017). the discretised latent space, e.g., vq- vq-VAE (Oord et al. 2017). to the conditional variant and its recent advents, e.g., semi-supervised VAE (Kingma et al. 2014), NVAE (Vahdat & Kautz 2020).
CVAE (Sohn et al.) 2015), and the Gaussian mixture VAE (Dilokthanakul et al. 2016). Falling into the last category, our proposed method can be regarded as a novel type of conditional VAE that embeds a clustering concept. To interested readers, we attach a detailed methodological comparison between our approach and mentioned alternative approaches in A.I Additionally, rather than the conventional usage in the work of Zhu et al. (2020), our implementation of the contrastive learning directly applied the embedding space instead of the original feature space.
DUAL UTILISATION DIAGNOSTIC INFORMATION
To exploit diagnostic information, i.e., the discrete diagnostic labels c E C, in learning low di- mensional latent variables in the embedding space Z that are aware of both projected high dimensional FC input features x E X, and their diagnostic attributes. as shown in Figure T we delineate dual utilisation of diagnostic information in our proposed conditional VAE.
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1. The provided diagnostic information first serves observable variables to regularise the learning of in a novel type of conditional VAE, ensuring the learned (low- dimensional) to be clustered around close-to-empiricnt cluster centers.
2. The associated diagnostic difference is further encoded in the embedding space via contrastive learning.
b.
Manner-1
a.
Manner-2
0
High dimensional functional connectivity
X
Low dimensional embedding
Z
Diagnostic information
c.
C
daagnosicc difference
Dual utilisation of diagnostic information in novel type of conditional VAE. a. The scheme of dual utilisation of diagnostic information. The red dotted line represents the Manner-1, 1, i.c. provided diagnostic information serves discrete variables facilitate the projection high- dimensional FC space into low-dimensional embedding space (the red dotted line in subfigure b), whereas the blue dotted one denotes the Manner-2, i.e., the modelling of diagnostic difference in the projected low-dimensional embedding space. Both manners are incorporated into our derived novel type of conditional VAE. b. The graphical model rendering of our proposed novel type of conditional VAE, which encourages the encoded to be clustered in accord with their corresponded disorder phenotypes. Solid lines denote the generative processes; dashed lines refer to the inference processes. Unshaded circles denote the unobservable variables, whereas the shaded ones represent the observable variables. c. The thumbnail of the second manner: encoding the diagnostic difference in the embedding space through contrastive learning.
Figure 1:
3.1 MANNER-1 1: REGULARISE THE LEARNING OF 2 IN A NOVEL CONDITIONAL VAE
Under the coinage of VAE, an amortised joint inference distribution qo(2, x) go (=(x))(((z) is com- monly employed to approximate the intractable joint generative distribution (2, = (z {))(2)). Aside from the empirical distribution p(z). we further assume p(2) as Normal distribution, and both qa (2(xr) and po (x|²² are conditional Gaussian distributions in which location and scale parame- ters can parameterised by complex neural networks. The optimisation objective is acknowledged the minimisation of the K-L divergence between two joint densities of latent variable and input, ie., p(z, æ) and q(2, x). as: DKL {4s (z, x:) (2,
To harness provided diagnostic information, we explicitly add discrete variables E C' to represent available diagnostic information; it leads to a conditional VAE (Figure (b)). which the general optimisation objective be derived DEL (q0(2, x, (plppp c))) Crucially, further spec- ify the inner components of two joint densities (2 c), and (o(2. x, c). we adopt a hierarchical encoding process that admits the following derivation: (6) (c, 2|x) qa. (cl=)4s, (z(x)) In
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similar vein, hierarchical decoding process permits: Pto.,0.)(2, 21))) x po, (2l2))o (z/c). As
sult, the foregoing joint inference and generative distributions can be derived as: [o..d..) (2, x qo. (cl (2|x)p(<) and ol) (2, e) & Pa_ (e_((r))pp__(ce)p respectively. From now on, we ignore subscripts for brevity. The general optimisation objective of this novel type of conditional VAE Levae with specified hierarchical inference and generative processes can be rewritten into
novel format:
DKL (e (),,
Σ lf ((d)))(()))))())) log p(z p(z12)p(21e)p(()
((cl)) (((2)()))))) dzdæ
(<2(x) p(=[c)
E Ez)((1) E B Expp(a) (kolo(d=)(l((e)
Σ (clz)) log
where we ignore the optimisation irrelevant term log p(z) (an empirical distribution) for brevity, With N number of classes, we define a categorical prior distribution Cat(clT), π E R for p(c). Three different parameterised Gaussian distributions are further defined as p(a[2) [ax pa, I), (<222)) M2. (2. and p(z[c) = ((2 Me, Me, I), respectively, where I is the identity matrix. (<llz) can be modelled by a multi-layer feedforward soft-max network to produce categorical representation (logits) for each encoded latent representation.
The first component in equation i.e., Eg~g(=|=) log p(r]()))) can be realised as the conven- tional reconstruction loss in vanilla VAE to ensure the low-dimensional latent variables encoded
from high-dimensional FC features. The entire second component, i.c. Dx (xz({(<))]]l(()) +
g(sla) (<(2)) log p(aeef+ can be understood as the imposed (implicit) clustering effect in the low- dimensional embedding space.
The first term in the bbove-mentiones second component, i.c., DK (((c)))(p(())) serves as pre- ventative measure to prevent creating dominant cluster in the face of a cass-imbalancee dataset. Intuitively, it encourages the produced categorical representation (logits) from g(cl2) to be close to its categorical prior distribution p(c). However, since the K-L. divergence is intractable di- vergence measure, we compute an easy-to-measure alternative quantity, i.c., the cross-entropy of two distributions H (g(c]2), p(c)) in practice. Letting N indicate the number of input classes, the
second term in the second component, i.e.E. ((c]=) log p(aie) encourages the learned embedding space to be divided into N different subspaces to ensure each encoded latent representation can be ascribed of N clusters. Since the loss term that on the clustering effect can be pinned down to fl|== 1el12 (see the derivation in attached A.2). where the cluster centre fr, is provided directly from the precomputed empirical cluster mean, first utilisation of diagnostic information can be regarded as enforcing each encoded embedding to be closer to its belonged cluster centre /e within each N clusters.
g(>[z)
3.2 MANNER-2. 2: ENCODING DIAGNOSTIC DIFFERENCE IN THE EMBEDDING SPACE
Corresponding to the second manner of utilising diagnostic information, i.c., embedding the di- agnostic difference between disorder phenotypes, we resort to contrastive learning (Hadsell et al. 2006) to explicitly model this diagnostic difference. As shown in Figure [(),, we define a pair of latent vectors zi, 2/2 Z represent tion D to measure the dissimilarity of two latent vectors, and a binary class indicator C = 0, 1 to verify whether latent vectors come from the same disorder phenotype. The contrastive loss function Leir (Hadsell et al. 2006) in our case can be defined as:
encoded high dimensional brain features, distance func-
(1- (1-C),, (D (1-C))(DDE)))
C [max(0) m D(z
where m is a pre-definable margin to weigh the contribution of dissimilar pairs, and we adopt the simple euclidean distance to capture D æ i.e. D(zt,z)) ||žÍ 2 2|2 To preserve the authentic diagnostic differences among encoded latent variables, the m is set to 0, i.c., we weigh the equal importance of dissimilar and similar pairs.
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Importantly. bridging the two loss terms, ie., Leir and Levae together, overall loss function Liotal through a convex combination of two terms can be derived as:
Liotal (1 A)eevae + XLerr
where we enforce the equal contribution of two loss terms, i.e., 0.5, to stress the equal im- portance of two manners in utilising the diagnostic information to derive our targeted embedding space. In attached A.3 we show the effects of differentiated shaping the embedding space for interested readers.
4 ASSESSING THE IMPORTANCE OF DIAGNOSTIC INFORMATION
To assess the importance diagnostic information in deriving the optimal low-dimensional embed- ding of neuropsychiatric FC features, we implemented our proposed method in simulation study, along with the ground truth embeddings. The purpose of this simulation study is twofold. (1) We seek to assess the importance of diagnostic information in recovering ground truth embeddings. (2) We also aim to demonstrate the empirical superiority of our proposed approach over several existing supervised representation learning approaches.
In preparing for the synthetic high-dimensional FC features, we strictly follow the following three- step generation procedure.
We firstly pre-train U-map projection function F on a real FC dataset, e.g., the SRPBS multi-disorder brain connectivity database (Tanaka et al.) (02T).
2. 300 low dimensional embeddings are then sampled from three 2D multivariate Gaussians (0.8, (0.44(0.4 1).22 1). The pair-wise distance relations among three 2D Gaus- sians are served as the ground-trune relation (see Figure 2
3. The inverted projection function F-1 is further utilised to map the foregoing 300 em- beddings from the low-dimensional (2D) ground truth embedding space into the high- dimensional FC space. In the case of the SRPBS dataset, this produces [300, 9730) synthetic FC feature matrix.
4.1 MODELS AND THEIR IMPLEMENTATIONS
4.1.1 WITHIN OUR APPROACH
The crux of implementing our proposed approach to compute the final loss function equation 3
with its involved loss terms Levae in equation] and Ler in equation 2 Aside from the easy com-
puted reconstruction loss, in the remaining components of equation i.e., Σ. 7(cl2) log g(z(n)
p(=|c)
the parameters that are pertaining to the optimisation of Σ. g(c|z) log p(ale)
g(=lz)
can be reduced to of. Ma. 2, and Mec As or and Ma can be parameterised by a feed-forward neural network, it allows the production of z via the re- re-parameteristion trick Kingma NN(0, 1). The cluster-wise mean P4c can be produced by simple one layer Gaussian network with supplied supervision signals, e.g., cluster labels in this simulation study. A simple softmax network is sufficient to measure the KL-divergence between <<c/2) and p(c). The second loss term Cer in equation 2 can be easily captured via adding a margin (distance) based feed-forward neural network on top of encoded latent representations.
Welling (2013),
i..., X t
Essentially, to demonstrate the importance of dual utilisation of diagnostic information in our pro- posed approach, corresponding to bboveemmetioned two utilisations, three instantiations of our ap- proach are established ranging from the full implementation of our model (Full model) 1) to the mere implementation of Lavae (Manner-1 model), and Car (Manner-2 model). Their layer- wise configurations and respective parameter estimation processes are concisely summarised in at- tached B.
4.1.2 ALTERNATIVE APPROACHES
Three categories of alternative methods are also included in this simulation study, ranging from the conventional sumi-supervised VAE (S-VAE) (Kingma et al., 2014), the mainstream manifold learn- ing based dimensionality reduction approach, e.g., supervised Umap (S-Umap) (McInnes et al.,
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201 8
to the metric learning based neighbourhood component analysis (NCA) (Goldberger et al. The implementation details of these alternative approaches are also demonstrated in attached
2004
B.1
4.2
PERFORMANCE EVALUATION
As shown in Figure 2 provided with ground truth embeddings and their pairwise distance relations, we target on evaluating the performance of various models regarding the capability recovering the ground truth embeddings from synthetic high-dimensional FC features. The evaluation metrics include the qualitative visual inspection of yielded embedding spaces, and a quantitative assessment to verdict whether the distance relations among estimated clusters are preserved.
To quantify the between-cluster distance on the derived low dimensional embedding space, we rely on the pair-wise Wasserstein distance-basee based Frechet inception distance (FID) based metric (Heusel ett al. 2017), i..,, FID (ID( ( N2)), over the conventional consideration both first and second degree statistics in measuring the discrepancy between two multivariate Gaussians. Its computation can be defined as:
and distance metrics for its
FID (in F1
11
/21² tr(E1
2(E))))4/))
where E are the first and second degree statistics of inferred low-dimensional multivariate Gaus- sians, i.e., (M, 2). On the basis suggested FID metric to assess the between-cluster relation, the provided ground-truth relation among three clusters can be expressed into the three-way inequal-
ity: FID(Cluster) 1 -Cluster3) F FID(Ctusterl -Cansterr)) < FFI (Clnsterr- Cluster3)
Juxtaposed in demonstrated Figure 2 among three instantiations of approach, the maximum utilisation diagnostic information, I.c., the Full model, achieved the most promising result on recovering the correct distance relation among three instantiations of our approach, along with the assessed significant pair-wist t-tests among three between-cluster distances in our Full model (see attached[)).
This observation, in conjunction with the consistent result in the second simulation study (see at- tached D) greatly substantiates the importance of diagnostic information in deriving an optimal low-dimensional space for high-dimensional FC like features. The superiority of our approach over included alternative ones are presented less-distorted low-dimensional embedding space and recov- ered accurate distance matrix. This observed empirical superiority of our approach assures the further implementation on real neuropsychiatric FC datasets.
MINING THE NOSOLOGICAL RELATION AMONG MULTIPLE DISORDERS
After demonstrating the importance of diagnostic information learning low-dimensional embed- dings of FC features, seek to implement our approach, i.e., the Full model, on curated neu- ropsychiatric FC datasets to explore nosological relations among disorder phenotypes. short, high-dimennional FC features of included neuropsychiatric disorders firstly projected into common oow-dimensional (2D) embedding space, where their pairwise distances are served as the nosological relation in this embedding space.
5.1 DATASETS METRICS
To acquire a high-quality neuropsychiatric FC dataset that contains multiple types of disorders is a time-consuming task, which demands expertise in diagnosis, and consistencv in ncuroimagine scanning protocols. The SRPBS multi-disorder brain connectivity database (Tanaka et al. 2021 that pools the fMRI scans of diverse neuropsychiatric patients from 8 different scanning sites with consistent diagnosis guidelines, perfectly serves research database.
UTO dataset From the opted SRPBS database, two datasets were formed to suit our need in study- ing the nosological relation among multiple neuropsychiatric disorders. The primary dataset comes from the data that were acquired at the University of Tokyo Hospital (AKA., UTO dataset), which is
The URL link for this database be referred https: / http://ii.rrrrssc.e.eeceo jp/ hp/srpbsfa/

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Cluster-1
Cluster-2 Cluster-3
Ground truth
Full model
Manner-1 model
Manner-2 model
Ghate
Costar
Cletert
Gutar
Quter
Gasted
Gosta
16
Qudte
Cudtent
Cheteen
Clusee-11
Quster-2
Quster
S-VAE
S-Umap
NCA
Quster-1
Guster-2
Ouster-3
Outen
035
929
Cheter
8:33
22
Orten
8.25
0.21
Figure 2: Model performance on synthetic FC features. The attached distance matrix is based on the computed FID distance between clusters in the derived low-dimensional embedding space. For each evaluated model, both the learned embedding space and the computed distance matrix were averaged over 20 runs of implementation.
comprised of IMRI scans from 3 chosen types of clearly diagnosed disorders: autism spectrum dis- order (ASD; 10 subjects), major depressive disorder (MDD; 62 subjects), and schizophrenia (SC7; 35 subjects). These fMRI scans were acquired protocol, which is free from the common threat of site difference in most fMRI studies. The brief introduction of the SRPBS database can be found in attached
the same facility under the identical scanning
The subject-wise brain features, i.e., functional connectivities, were obtained from 10-min resting- state fMRI BOLD signals that underwent the identical preprocessing steps. To compute the subject- wise functional connectivities from these preprocessed BOLD signals, the Brainvisa Sulci Atlas parcellation scheme, which each individual image was divided into 140 regions (Perrot et al. 2011) and the standard Pearson R correlation method were adopted for time series extraction, and the com- respectively. Hence, for each subject, 9730 connectivity features
putation of connectivity matrix ! were crafted serve as high dimensional brain features, forming [107, 9730] brain feature matrix.
HuShoWa dataset Aside from the main UTO dataset, a replication dataset from the SRPBS database were further curated from the Hiroshima University and ShoWa University (HuShoWa dataset). A total of 307 subjects were included in our HuShoWa dataset, occupying categories of MDD (173 subjects), ASD (115 subjects), and SCZ (19 subjects). Underwent the identical prepro- cessing steps, and the feature extraction process, we harvested a [307, 9730) FC feature matrix. To minimise the effect of site-difference on acquired fMRT scans in HaShoWa dataset, we resorted to the harmonisation technique, e.g., the combat approach (Yu et al. 2018) to correct site-difference in our input features.
To case the computation, the lower diagonal of connectivity matrix is reserved for connectivity features.

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Validation scheme & performance assessment Since empirical neuropsychiatric FC features seldom accompanied with ground truth low-dimensional embeddings, is essential to obtain strict validation scheme to ensure the reliability of our attained nosological relations. For this, opt for 10-fold cross validation scheme that demands 10 times running of our model on two empirical datasets.
In consist with the previous harnessed FID metric assessing the between-cluster relation, in both UTO and HuShoWa datasets, we applied the FID metric compute the cluster-wise FID distance on the basis of yielded low-dimensional embeddings of three disorders, e.g.. MDD): FID (ASD (AAAASCS)) (MDDMDD-SS
their inequality may inform us the noso- logical relation among ASD, MDD, and SCZ. Over the implemented 10-fold cross validation, mul- tiple paired t-tests in testing the statistical significance of these pair-wiss wise FID distances were also conducted to assess the reliability of attained nosological relations.
5.2 DISCOVERED NOSOLOGICAL RELATIONSHIPS AMONG ASD, MDD AND SCZ
As shown in Figure (left panel), on low-dimensional embedding spaces that are derived from the UTO and HuShoWa Wa datasets, with maximum utilisation of diagnostic information, the learned em- beddings are compact, clear-cut clustered in accordance with their represented disorder phenotypes
UTO
1-
M550
200 900
100-222
100-3:2
HuShoWa
M00
12.00
Figure 3: The discovered nosological relation among ASD, MDD, and SCZ across the UTO and HuShoWa datasets. Left panel: learned embedding spaces that are derived from the test data of UTO and WaShoWa datasets through the average over 10-fold cross validation. Middle panel: the computed FID distance matrices based on the averaged FID distances over 10-fold cross validation. Right panel: the conducted paired t-tests on pairs of FID relation of three disorders 10-fold cross validation. The training configuration for this empirical experiment is recorded in attached B.2
In respect the attained highly consistent embedding spaces (Figure 3 left panel), two simi- lar FID matrices (Figure 3 middle panel), and high statistical power in conducted paired t-tests (Figure 3 right panel) across two curated neuropsychiatric datasets, it unveils us an interestingly between-phenotype relation among the considered ASD, MDD and SCZ, i.e., F FID
TTo interested readers, in attached demonstrate the empirical comparison of the derived embeddings from approach and several competing alternatives Ha dataset.
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SCC2) and FID (ASD- FID(MDD-SC2) This suggests that ASD and MDD may belong to two independent nosology entities, which are loosely connected regarding their large differences in corresponding neural correlates (functional connectivities), whereas the closely linked ASD-SCZ, MDD-SCZ nexuses indicate the potential nnosological closeness' between SCZ and the other two disorder phenotypes, which is in line with the previous findings high rates of comor- bidity on SCZ-ASD [Wood] 2017) and SCZ-MDD (Tsai & Rosenheck (0013).
Based on this finding, we are able to align these disorder phenotypes on an FC informed dimensional coordinate, allowing ASD and MDD to situate at two ends of this coordinate, in correspondence with two domains of psychopathology, i.e., the neurodevelopmental and affective pathology, respectively
(see attached G (Craddock & Owen 2010).
5.3 NEUROSCIENTIFIC INSIGHT
Furthermore, since the final projected FCs were represented as two-dimensional features, of increasing research interest to see whether these two woo-dimensional features are in agreement with ex- isting disorder related connectivities in the brain space. For this, harnessing the layer-wise relevance propagation method (Montavon et al. 2019), we have projected each "cluster centre' (the most repre- sentative feature in each of three clusters) back into the high dimensional FC space, and tentatively identified significant connection between Caudate and Cuneus that obtains the highest rele- vance in discriminating three disorders (MDD, SCZ, ASD) among the overall 9730 FCs. However, the validity of this preliminary neuroscientific insight involved functional connectivity is under the threat of used nonlinear projections (multiple ReLU connections in both inference and generative networks), where the isomorphism between the -wwddimesioonl embedding and nigh-dimensional FC space is not guaranteed.
Lastly, to assess the interpretability of learned low-dimensional embedding space in our context, we focus on probing the potential association between learned embeddings and measured clinical or psychological assessments. On the basis of provided ASD related autism spectrum quotient (AQ
score) (Baron-Cohen et al. 2001), and MDD related Beck Depression Inventory (BDI score) (Beck) al. 1996) in the HuSho Wa dataset, the associations between our derived embeddings and the clinical factors are captured, i.e., AQ score: p = 0.533, p < 0.001 and BDI score: p 0.540, p < 0.001, suggesting the potential clinical awareness of derived low-dimensional embeddings.
LIMITATIONS
Two limitations of this study are the confined capability of our approach in discerning novel sub- type of a disorder or the nosological overlap between two disorders, and the scarcity high-quality independent FC datasets that are curated other than the harnessed SRPBS database. The former limitation may ameliorated through investigating the finer (possibly hierarchical) structure within low- -wwdimmnsionnl embeddings of known phenotypes, whereas the latter one may be tackled by the future release of the high-quality imaging datasets from the Brain/Minds beyond MRI project (Koike al. 2021
7 CONCLUSION
Unveiling the complex nosological relation among diverse neuropsychiatric disorders with the help of diagnostic information, advocate neural correlate based approach, which targets on deriv- ing a neural correlate informed low-dimensional embedding space. To learn low-dimensional em- beddings of neural correlates that preserves the diagnostic attributes, dual utilisation of diagnostic information that is incorporated in a novel type of conditional VAE, are proposed in this research. Relying on this method, we discovered a reliable and consistent nosological relation among ASD, MDD, and SCZ across two curated neuropsychiatric FC datasets.
Undoubtedly, the current approach- its embryonic stage faces several mentioned limitations, future approach with enhanced interpretability on the obtained low dimensional embedding space, and the inclusion of an independent validation dataset should enable us attain more reliable noso- logical relations on various neuropsychiatric disorders to encourage the age-old, discrete categorical view on mental illness to make an exit at a fast pace.
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ETHICS STATEMENT
Concerning the potential ethnic issues of our propose approach serving as a clinical application, raise four concerns on future use of approach.
Undoubtably, the sole reliance on the discovered nosological relation on basis of our ap- proach is neither optimal nor responsible in the diagnosis procedure. Given the early stage of our approach, harnessing the discovered nosological relation among disorders is still immature to replace established symptom-based diagnosis in clinical psychiatry.
2. Regarding discovered nosological relations among disorders, given the heterogeneous, complex, and developmental nature of most neuropsychiatric disorders, further validation of such relations is demanded.
3. Meanwhile, since our probed nosological relation among disorders on 2D embedding space is induced from the computed distance relation, it does not imply any potential signs of comorbidity.
4. To yield a wide range of social acceptance on using neuroimaging modality in clinical diagnosis, it demands more future validation studies that come from other aspects, e-g., the genetic aspect, and symptom relevance. Solely relying on the neuroimaging modality in clinical diagnosis be misleading and unethical.
Unless these raised ethical concerns are addressed, along with additional validation studies and evidence, clinical use of our approach should not be considered and recommended at this moment.
REPRODUCIBILITY STATEMENT
To boost the reproducibility of our study, the executable Python code snippet that allows the implementation our model contained within the uploaded Zip file. Within the uploaded Zip file, we also include code snippet that contains the helper function we used in the com- putation of FID scores between embeddings. The used SRPBS database can be referred to https: //btss:/biirrr resource, atr. jp/ ssppsf/p The public available version of this ferred database can requested upon the site.
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A
METHODOLOGICAL DISTINCTIVENESS & DERIVATIONS
A.1 METHODOLOGICAL COMPARISON BETWEEN OUR APPROACH AND ALTERNATIVES
As summarised in following Table 1 the main difference between our approach and NVAE (Vahdat
& Kautz, 2020) lies on the derived hierarchical process in defining the decoding and generative process. In NVAE, the hierarchical process can be seen in the sequential production of 2, i.e., 2 :1,... Z1; (32 (r) = {(>2|=1, This sequential encoding and decoding procedure allows NVAE to implement feature combinations from
zt. Differ to NVAE, our hierarchical process focuses on the conditional generation of the latent variable from both discrete variable c, and the input æ, ie., 2(c, ((()2) More importantly, with defined categorical prior distribution for the discrete variable (c((r) т & R. for p(c), the N groups of are encouraged to be mutually independent, i.e., the implicit clustering effect. Such independence is neither the objective the outcome of NVAE approach.
The hierarchical process in our inference model entails the conditional chain ((c)=))(=(r))px)) whereas in CVAE (Sohn et al. 2015), such conditional chain is replaced by the conditional den- sity q<2|<r, y). In similar vein, illustrated in Table I the major distinction of our approach conventional semi supirsupee VAE (Kingma et al. 2014 also lies on their differentiated derivations on inference networks.
In regards to different instantiations on graphical models, our approach also differs from the Gaus- sian mixture VAE on both inference and generative networks. In Gaussian mixture VAE (Dilok- thanakul et al. 2016), the inference network is defined as q(x, w, 2/c), and its corresponded gen- eration network is p(c, æ, =) x p(æ|w, =)p(clzr) Ignoring the Gaussian mixture variable w, the foregoing networks can be seen as q(2, z/c) x q( ((=(=)q(<())) and p(<(=)p(c(zz) In our networks, these two processes are defined as (())))((2(()) and p(æ ())cc)))) respectively. Aside from these differences, we acknowledge the explicit modelling of different Gaussian mixtures w, which cannot
be achieved by the current version of our approach.
TABLE 1: MODELLING DIFFERENCES BETWEEN OUR APPROACI AND ALTERNATIVES.
MODEL
GENERATIVE NETWORK
INFERENCE NETWORK
NVAE
[lpp(z)) B.c pp 2))
[lg aec, 2)p(2) <(=|a, c),(2,
CVAE
p(2)=)p(c(a, 2)
SEMI SUPERVISED- VAE
p(a|2, ()ppec))(()
<=aar e c)p(z)p(c)
GAUSSIAN MIXTURE VAE
p(s))))((())p(e)
((3)))
OUR APPROACH
p(z)=)p(z(c)p(c)
(2
A.2 DERIVATIONS
In Section 3.1, we put forward novel type of conditional VAE that allows implicit clustered em- g(=lz)
beddings. The core component of our loss function pertaining to log p(sje)' where we embed the implicit clustering effect. Given two conditional Gaussians to parameterise g(2[z) and p(2]c), respectively, i.e., q(2(x:) V()a,02). and p(zlc) M(pe, 7), the general optimisation loss term
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for this component can be derived into:
g<3/x) p(=|c)
= log N(2z oi
log
Mrr [2]
{- 1
where N (H,, 0²) = V2TO²
exp
and W 1)= (2x)
exp {-2 || - rell
2
xx
1
1
m(x)
-2 --
log o2
as 2 Ms + 6 o2 (0,
0 (x)
1
2| Mell
log o
||
2
2
2
Ignoring the last constant term ||e|l", we arrive at
1 || 12elll
log o
2
2
(4)
From the following general derivation, it clearly indicates the contribution of μr in producing our targeted low dimensional embeddings Le., from the regularisation viewpoint, Phe can be viewed as one type of regulariser to regularise the learning of 2.
A.3 THE IMPORTANCE OF À IN DETERMINING THE SHAPE OF 2
A - O 0.1
0.3
0.5
0.7
0.9
X
Figure 4: The role of À in our proposed approach. With the increment on the value X, the cluster- wise distances enlarged, along with the increasing amount of void space among clusters.
In the main text, to balance the contribution of two manners, we pre-define the X = 0.5 to enforce the equal weighting between two manners. However, this À value be tuned flexibly in determining the shape of each embedded cluster. In the attached Figure [] through the demonstration on MNIST dataset, show the effect of differentiated À in controlling the overlapping degree of encoded clusters. Le., À + 0, clusters of embeddings tend to be overlapped with each other, whereas À + 1, encoded clusters are pulling away from each other, in the contribution of the independent, non- overlapping clustering effect.
B TRAINING CONFIGURATIONS
B.1 CONFIGURATIONS IN THE SIMULATION STUDY (SECTION 4.)
The following two subsections presented here to demonstrate the default configura- tions for implementing three instantiations of our proposed approach, i... Full model, Manner-1 model, and Manner-2 model, and three alternative models, i.e. S-VAE model, S S-Umap model, and NCA model, which were utilised in Section of the main text.
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Table 2: Model configurations of three instantiations of proposed approach.
Instantiation
Configurations
Network-1 1 (2) #1: FC 9730:2000222000.973,, ReLU activation
2 (c): FC 4000; 2000:100, ReLU and Soft-max activations
Full model
N (Car ):FC 40, ReLU activation, D: Euclidean distance
À 0.5, Batch Size: 20, num of batch: 20
optimiser:Adam, KKngmaa & Ba 2014)
Network-1 (c):FCC9773:22022220000
ReLU activation
Network-2 (c): FC 4000;2000;100 ReLU activation
Manner-1 model
Batch Size: 20, num of batch: 20
optimiser:Adam (Kingma & Ba 2014)
FC 40, ReL.U activation
Manner-2 model
D: Fuclidean distance, Batch Size: 20, num of batch:
optimiser:Adam (Kingma & Ba 2014)
Table 3: Model configurations of three alternative approaches
Model
Configurations
Network-1 I (2): FC 500, ReLU activation
Network-2 (c): FC 500, FC 9730, ReLU and Sigmoid activations
S-VAE model
Network-3 (ft)): FC40;3, ReLU and softmax activation
Batch size: 20, num of training epoch: 20
optimiser: rmsprop, (Hinton al. 2012)
num of components: 3, num of neighbours: 10
S-Umap
space: Euclidean space
of training iterations: 1K, convergence tolerance: 1e-1
num of components: 3,
NCA
initialisation approach: LinearDiserininantAAaayy (Duda et al. 2006
num of training iterations: 1k, convergence tolerance: Le
B.2 CONFIGURATIONS IN THE EMPIRICAL STUDY (SECTION 5.)
The following table summarises the training configuration of our approach in exploring the nosolog- ical relations among various disorder phenotypes in two empirical datasets (Section of the main text). To improve the reliability of the attained nosological relation, adopt the nearly identical model configurations to implement our model UTO and HuShoWa datasets, respectively. Re- garding the scarce sample sizes of two datasets, the complex cross validation procedure may not be appropriate here. Therefore, in Figure of the main text, the results were yielded from two full datasets.
Table 4: Model configurations in UTO and HuSho Wa datasets.
Dataset
Configurations
Network-1 I (2) #1: FC
ReLU activation
Network-2 (c): FC 4000; 2000;100, ReLU and Soft-max activations
Network-3 (Lar)::CC 40, 20, ReLU activation, D: Euclidean distance
À 0.5, Batch Size: 1024, num of batch: 500
UTO
optimiser.Adam. (Kingma & Ba 2014)
HuShoWa
number of training epochs: 80 batch size: 256
the usage of early-stopping: No
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C STATISTICAL TESTS
To assess the statistical significance of attained embedding relation in the above- simula- tion study (see Main text Section 4.), we conduct series of paired t-tests assess the significance of pair-wise between-cluster FID across six different approaches. As shown in the following Fig- 5 our proposed Full model achieved the most superior t-test results among harnessed approaches, albeit the comparison between C2-C3 and C1-C2 does not achieve the statistical signif- icance. Note here, the observed strong statistical significance between C2-C3 and C1-C2 in NCA, i... C2 C3 > C1 C2, contradicts with the ground i..., C2 C3 < C1 C2.
Manner-1 model
Manner-22
223
2
S-VAE
S-Umap
NCA
010
8=
2-
Figure 5: Paired t-tests in assessing the statistical significance among the computed between-cluster distances across 20 runs of implementation. All reported significant t-test scores are Bonferroni corrected. Only t-test scores that are statistical significant, are reported in this figure. Abbreviation index: C1/C2/C3: Cluster1/2/3.
D THE SECOND SIMULATION STUDY
We attach supplementary (2nd) simulation study (see the following Figure to further solidify the observed empirical superiority of our approach, i.e., the full model, in recovering low-dimensional embedding of FC-like digh-dimensional features. In this attached 2nd simulation study, all set-ups are kept the same with the 1st simulation study (Section 4. main text) except for the non-linear scattering of simulated ground-truth embeddings. Inconsistent with the rendered 1st simulation study, the only model that recovers the ground between-cluster relation is our proposed Full model.
E SRPBS DATABASE & DATA PRE-PROCESSING STEPS
The employed UTO and HuSho datasets are derived from the SRPBS database, which con- sisted of resting-state fMRI scans from 8 different sites. The detailed curation and pre-precessing steps of SRPBS database can be consulted in the work of Tanaka et al. (022)) Here, we highlight some of the key aspects of scanning and data pre-processing. At each site, the subject-wiss resting- state fMRI data were obtained from 10-min resting-state fMRI scanning session with the eye-open condition. For data curated in HuSho Wa dataset, the imaging data acquired in Siemens Spec-
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model
Manner-1 mode|
model
Cluster-2 Clusten-
Ground truth
S-Ummpp
Figure 6: The second simulation study on synthetic FC features. line with the 1st simulation study in the main text, compute the distance matrix the basis of between-cluster FID in the derived diwdeennsional embedding space. The training configuration of each model is identical to the 1st simulation study.
tra and Siemens Verio scanner, whereas for the data in UTO dataset, data were acquired from GE MR750W scanner.
All resting-state fMRI data underwent the identical pre-processing step include slice-time correla- tion, realignment, co-registeration, segmentation Te-weighted structural images, normalisation to Montreal Neurological Institute (MNI) space, and spatial smoothing with an isotropic Gaussian kernel of mm full-width at half- malf-maximum.
F EMPIRICAL COMPARISON OF DERIVED EMBEDDINGS FROM OUR APPROACH AND ALTERNATIVE MODELS
Implemented on the same HuShoWa dataset, empirically evaluate the derived low dimensional embeddings from our approach, and several alternative ones, e.g., neighbourhood component anal- ysis (NCA) (Goldberger et al. 2004), T-SNE (Van der Maaten & Hinton 2008), Supervised Umap (S- (S-Umap) (McInnes et al. 2018). and a conventional semi-supervised VAE (S- VAE) (Kingma et al. 2014).
our approach
NCA
T-SNE
S-Umap
S-VAE
Figure/ 7: Empirical comparison of embeddings derived from our approach and several alternative approaches on HuSho Wa dataset.
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G DIMENSIONAL VIEW ON ASD, MDD, & SCZ
Demonstrated in the following Figure g in accordance with the envisioned dimensional view on diverse neuropsychiatric disorders (Craddock Owen 2010), recast the attained the nosological relation among ASD, MDD, SCZ on the following continua, where ASD and MDD reside at two poles of this continua.
Neurodevelopmenntal pathology
Affective pathology
SCZ
MDD
Figure 8: The dimensional view on ASD, MDD and SCZ.
19


Reviews:
[{"review_id": "ok3J-xb6KOf", "reviewer": "Reviewer_94nu", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "STRENGTHS:\n\n1. The paper is well written, self-contained, and easy to follow. The methodology and main technical contributions are clearly articulated and explained. Moreover, the authors demonstrate experimental results over two separate clinical datasets independently.\n\n2. As per my knowledge, the clinical problem of interest, i.e. a dimensional mapping of various psychiatric disorders (as opposed to diagnostic classification) is a relatively less studied problem in (data-driven) functional connectivity analysis. The methodology of the paper could serve as a tool to expand the psychiatric characterization offered by functional connectivity analysis for clinical applications.\n\nWEAKNESSES:\n\n1. Clinical Motivation:\n \nThe authors motivate the need for their framework by comparing their problem of interest with the categorical characterization (case/control) of neuropsychiatric disorders. Given the lack of such a  “ground truth” dimensional characterization available apriori, the results are hard to draw strong conclusions from. Moreover, motivation wise, it would be interesting to know how the problem on uncovering pairwise nosological relationships compares against a more fine grained prediction of continuous clinical severity indicators (such as the prediction of AQ scores for ASD or the BDI scores for MDD in Section 5.3)\n\n2. Evaluation on Real and Synthetic Data: \n\n(a) With the lack of ground truth optimal dimensional characterization of input examples beyond diagnostic labels, the results of the experiments are a bit hard to interpret beyond consistency over bootstrapped trails. Additionally, it is unclear how the intrinsic dimensionality of the optimal latent space is chosen and how this choice affects performance.\n\n(b) While the consistency of the cluster separation (among various disorders) is examined for their proposed model, the authors restrict the comparison with baselines to a qualitative assessment of the latent embeddings in Appendix C, rather than the full suite of quantitative metrics. They also do not perform these comparisons on ablated versions of their model. Thus, it is still a bit unclear what value the technical sophistication of the model adds to the real world studies.\n\n(c) It is unclear why the authors abruptly chose to report the FID distance metric in the experiments on real data alone. It seems to me that including this metric for the synthetic experiments could help illustrate the fidelity of the recovered clustering in the latent space better.\n\n(d) In Fig. 4, the statistical comparisons on the UTO dataset seem to not reach significance in terms of the proposed pairwise distance relationships they seek to uncover. At the same time, the authors subsequently argue about the consistency in the three way dimensional characterization without adequate explanation for these observations.\n               \n3. Validation Procedure: \n\nThe authors mention a 10 fold cross validation that is performed  on both datasets (UTO and HuShoWa). They also mention that “Regarding the scarce sample sizes of two datasets, the complex cross validation procedure may not be appropriate here.” Thus, it is unclear how they set various hyperparameters for their model.\n\n4. Clinical Interpretability: \n\nThe proposed model seems to offer limited clinical interpretability in terms of identifying the neural correlates or aberrant connectivity patterns that inform the uncovered nosological relationships. The discussion in Section 5.3 is largely heuristic in nature. \n\n(a) The authors restrict this discussion to an associative analysis of the embeddings onto selected disease phenotypes, and identifying a ‘significant connection’ by back projecting from the low dimensional spaces. It is unclear how the significant connection between the Caudate and Cuneus was identified as having the highest impact on discriminating SCZ, ASD and MDD? \n\n(b) Similarly, it is also unclear what the authors mean by isomorphism between the low-dimensional embedding and high-dimensional FC space? \n\nOverall, this discussion suggests that the proposed framework is somewhat limited in scope and clinical applicability.\n", "summary_of_the_paper": "The authors propose a novel variational autoencoder to utilize functional connectivity (FC) features from resting state fMRI (rs-fMRI) scans in order to uncover latent nosological relationships between diverse yet related neuropsychiatric disorders. The autoencoder  seeks to learn a mapping  from high-dimensional FC space to a low-dimensional embedding space that is constrained to preserve pairwise relationships between the diagnostic attributes of the disorders.\nFrom a clinical standpoint, this work studies the nosology of complex disorders using a continuous dimensional characterization rather than using categorical and discrete diagnostic labels for supervision.  \n\nThe authors validate their framework on both synthetic data and two separate clinical rs-fMRI datasets consisting of patients diagnosed with Autism Spectrum Disorder (ASD), Major Depressive Disorder (MDD) and Schizophrenia (SCZ). Their experiments evaluate the consistency of uncovered pairwise nosological relationships inferred from their latent representations.  They demonstrate that their model is capable of reliably inferring a dimensional characterization of multiple brain disorders beyond diagnosis labels.\n", "summary_of_the_review": "While the model  and problem being studied is interesting and offers some degree of novelty both in the technical and clinical space,  there are still several concerns with the evaluation and validation being performed. Additionally, the clinical scope of the proposed framework is also not as well justified. For these reasons, I choose to rate the paper as marginally below the acceptance threshold.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "DH4VV97L_JZ", "reviewer": "Reviewer_Cbpb", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "1. The English of this paper is poor which makes it difficult to read.\n2. I do not understand the purpose of this paper, for example, what is the use of the low-dimensional representation of the functional connectivity obtained by the algorithm? Is it to classify patients? However, using label information to guide dimensionality reduction obviously introduces data leakage problems.\n3. The author tries to use diagnostic information to guide the dimensionality reduction of functional connections. However, unlike other diseases, one of the main problems of mental illness is that there is no gold standard for the diagnosis, which relies on the description of subjective symptoms. This means that diagnostic information cannot guide the analysis of brain imaging data.\n", "summary_of_the_paper": "This paper presents a method to reduce the dimensions of resting-state functional connectivity for psychiatry disorders such as autism spectrum disorder, major depressive disorder, and schizophrenia. Their method is based on a conditional variational auto-encoder that utilized the diagnostic label. They evaluated their method on two neuroimaging datasets and obtained clustered 2-dimensional representations of multi psychiatry disorder.", "summary_of_the_review": "Extensive evidence showed that these neuropsychiatric disorders share symptoms and neuroimaging features. For example, several cross-disorder studies showed that major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ) share biological, neuropsychological and clinical features, despite the criteria for their respective diagnoses being different. (Chang, Miao, et al. \"Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning.\" Molecular psychiatry 26.7 (2021): 2991-3002)( Zhang, Luheng, et al. \"Three major psychiatric disorders share specific dynamic alterations of intrinsic brain activity.\" Schizophrenia research (2021).) Therefore, the utilization of diagnostic information will not help to separate different disorders. Furthermore, these mental illnesses are often misdiagnosed. For example, the diagnosis of 50.7% of mental disorder participants was changed in a 10-year follow-up study, including schizophrenia spectrum disorders, bipolar disorder, major depression disorder. (Bromet, Evelyn J., et al. \"Diagnostic shifts during the decade following first admission for psychosis.\" American journal of psychiatry 168.11 (2011): 1186-1194.) \nTherefore, I believe the clinical motivation of this paper is wrong. \n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "BoVJvbKnMJL", "reviewer": "Reviewer_odDt", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Minor comments:\n``Letting N to indicate\" should be ``Letting N indicate\".", "summary_of_the_paper": "This paper proposes a novel type of conditional variational auto-encoder that incorporates dual utilization of diagnostic information in learning an optimal embedding space for high dimensional functional connectivity features. The approach is further implemented on two empirical neuropsychiatric neuroimaging datasets to discover a consistent nosological relation among autism spectrum disorder, major depressive disorder, and schizophrenia.", "summary_of_the_review": "The paper is generally very well written, clearly presented, and a pleasure to read. The technique look sound and the contributions look solid.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "4txv8VtuIEz", "reviewer": "Reviewer_bgiV", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n\n- The paper presents a new conditional variational autoencoder model.\n\n- The approach uses the diagnostic information in 2 different ways to improve learning the low dimensional embedding.\n\n- Experiments include both synthetic (i.e. with ground truth) and real datasets. \n\n\nWeaknesses:\n\n- In the experiments, the authors compare to ablation versions of their own model and a few other models. I'm curious whether the authors have tested other conditional VAE models that the authors mention in the introduction (.e.g, CVAE by Sohn et al., 2015)? Also, whether they have tested mix of other models with their proposed \"manner 2\" loss, e.g. add the contrastive loss to the tested S-VAE. \n\n- The results in Fig 2 look very nice, but I am wondering how stable the results are? That is, across runs does the proposed method consistently produce the correct order of clusterings?\n\n- For the experiments on the real dataset, it is unclear exactly how the cross-validation results are presented. In Fig. 3 left panel, are the displayed clusterings from one fold of cross validation? In the right panel, are the box and whisker plots showing the FID values for the 10 folds? \n\n- While I appreciate the use of 2 datasets, I am wondering whether model generalization was tested, i.e., using the trained model from UTO, did the authors try testing that model on the HuShoWa dataset, and vice versa? Such generalization is really important in neuroimaging domain, as gathering of large datasets often require bringing together data across imaging sites.\n\n- Suppose the authors can find a consistent nosological relationship across neuropsychiatric disorders. Could the authors comment on the significance of achieving such an ordering, in terms of clinical relevance? \n\n- The writing in the paper needs improvement - there are many long and awkward sentences/phrasings that make it difficult sometimes to understand the authors' points.\n\nMinor comments:\n\n- Could the authors clarify whether the compared NCA uses any signal (ie diagnosis) supervision or is purely unsupervised? If purely unsupervised the comparison seems a bit unfair. \n\n- In Fig. 3 middle lower panel, the FID between MDD-SCZ is very small, compared to what we see visually for those clusters compared to other paired clusters - wanted to check if this reporting is correct?  \n\n", "summary_of_the_paper": "This paper presents a new conditional variational autoencoder (VAE) approach to learn a low dimensional embedding of neuropsychiatric disorders from resting-state functional connectivity data. The proposed approach uses diagnostic information in 2 ways: 1) to cluster samples from the same disorder together via the conditional VAE model, and 2) to separate the clusters using contrastive learning.  The method is compared against other dimension reduction approaches on both a synthetic dataset and 2 real datasets which showed a consistent nosological relation among 3 disorders. ", "summary_of_the_review": "My recommendation is based on the proposed new type of conditional VAE, which could be applied to any other type of dataset, and the fairly convincing results on both synthetic and real datasets. However, I am unsure exactly of the clinical relevance of the paper's stated purpose (of finding nosological relationships between disorders), and I feel a few relevant comparisons are missing.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "0-PjA-r6h81", "reviewer": "Reviewer_nZ9a", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "First of all I would like to thank the authors for this interesting piece of work. Below, I will briefly list the main strength and weaknesses of this submission and make suggestions for improvement:\n\nStrengths\n-\tThe authors provide a useful extension to existing work on VAEs, which appears to be well-suited for the target application they have in mind.\n-\tThe authors include both synthetic and empirical data as test cases for their method and compare it to a range of related approaches.\n-\tI especially appreciated, that the authors validated their method on the empirical data and also provide an assessment of face validity using established psychological questionnaires (BDI and AQ).\n-\tI also appreciated the ethics statement pointing out that the method requires additional validation, before it may enter the clinic.\n-\tThe paper is to a great extend clearly written.\n\nWeaknesses\n-\tIn Figure 2 it seems that Manner-1 use of diagnostic information is more important than Manner-2 use of this information, which calls your choice into question to set lambda = 0.5 in equation 3. Are you able to learn this parameter from the data?\n-\tAlso in Figure 2, when applying your full model to the synthetic data, it appears to me that inverting your model seems to underestimate the within-cluster variance (compared to the ground truth). Could it be that your manner-1 use of information introduces constraints that are too strong, as they do not allow for this variance?\n-\tIt would strengthen your claims of “superiority” of your approach over others, if you could provide a statistical test that shows that your approach is indeed better at recovering the true relationship compared to others. Please, provide such tests.\n-\tThere are important information about the empirical study missing that should be mentioned in the supplement, such as recording parameters for the MRI, preprocessing steps, was the resting-state recorded under eyes-open or eyes-closed condition? A brief explanation of the harmonization technique would also be appreciated. It would also be helpful to mention the number of regions in the parcellation in the main text.\n-\tThe validation scheme using the second study is not clear to me. Were the models trained on dataset A and then directly applied to dataset B or did you simply repeat the training in dataset B. If the latter is the case, I would refer to this as a replication dataset and not a validation dataset (which would require applying the same model on a new dataset, without retraining).\n-\tHave you applied multiple testing correction for the FID comparisons across diagnoses. If so which? If not, you should apply it and please, state that clearly in the main manuscript.\n-\tIt is somewhat surprising that the distance between SCZ and MDD is shorter than between SCZ and ASD as often the latter two are viewed as closely related. It might be helpful to discuss, why that may be the case in more detail.\n-\tThe third ethics statement is not clear to me. Could you clarify?\n-\tThe font size in the figures is too small. Please, increase it to improve readability.\n", "summary_of_the_paper": "The authors present a new conditional VAE, which uses diagnostic information as a supervision signal in two different ways, with the aim to identify small latent embedding spaces for functional brain connectivity that preserve diagnostic information. The authors have potential applications in computational psychiatry in mind, which are indeed extensively discussed in the field (transitioning to a continuous nosological approach to mental illness). They compare their approach on synthetic data to various related approaches and ultimately apply it to two functional connectivity datasets from patients with different psychiatric diagnoses, which appears to give good results.\n\nTheir main contribution is technical and lies in defining a new cost function to learn the embedding space. Their work incrementally builds on existing work, but appears to be sufficiently novel. There is also a moderate empirical contribution as they analyze the relationship between MDD, ASD and SCZ.", "summary_of_the_review": "Overall, I believe this to be a solid paper. There are some remaining issues that need addressing, before I can raise my score, for example pertaining to reporting and ensuring that rigorous statistics and quantifications are used to substantiate the authors' claims. If these comments are adequately addressed, I am sure, that this work would be a valuable contribution that hopefully will also be of interest to other research areas.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a novel variational autoencoder to utilize functional connectivity (FC) features from resting state fMRI (rs-fMRI) scans in order to uncover latent nosological relationships between diverse yet related neuropsychiatric disorders. The methodology and main technical contributions are clearly articulated and explained, and the experimental results seem convincing. On the other hand, the proposed framework is somewhat limited in scope and clinical applicability, and the writing in the paper needs improvement (as pointed out by two reviewers).
Decision: Reject