Generalizable Representation Learning for fMRI-based Neurological Disorder Identification
Authors: Wenhui Cui
Haleh Akrami
Anand Joshi
Richard Leahy
Venue: Accepted by TMLR ()

Abstract: Despite the impressive advances achieved using deep learning for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in tasks such as identifying neurological disorders. For functional Magnetic Resonance Imaging (fMRI), while data may be abundantly available from healthy controls, clinical data is often scarce, especially for rare diseases, limiting the ability of models to identify clinically-relevant features. We overcome this limitation by introducing a novel representation learning strategy integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features. This approach enables generalization to challenging clinical tasks featuring scarce training data. We achieve this by leveraging self-supervised learning on the control dataset to focus on inherent features that are not limited to a particular supervised task and incorporating meta-learning to improve the generalization across domains. To explore the generalizability of the learned representations to unseen clinical applications, we apply the model to four distinct clinical datasets featuring scarce and heterogeneous data for neurological disorder classification. Results demonstrate the superiority of our representation learning strategy on diverse clinically-relevant tasks.

Published in Transactions on Machine Learning Research (05/2025)
Generalizable Representation Learning for fMRI-based Neu- rological Disorder Identification
Wenhui Cui
wenhuicu@uus..edu
Ming Hsieh Department of Electrical ond Computer Engineering University of Southern California
Halch Akrami
akrumillusc.c edu
Ming Hsich Deparbment Electrical and Computer Engineering University of Southern California
Anand A. Joshi
ajoshi@usc. edu
Ming Hsich Department of Electrical and Computer Engineering University of Southern California
Richard M. Leahy
leahy Gusc. edu
Ming Hsich Department of Electrical ond Computer Engineering
Universily of Southern California
Reviewed on OpenReview: https: // openreview. net/ forum? id= #F9I+NT
Abstract
Despite the impressive advances achieved using deep learning for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity imaging data still pose challenges in tasks such identifying neurological disorders. For functional Magnetic Reso- nance Imaging (fMRI), while data may be abundantly available from healthy controls, clini- cal data is often scarce, especially for rare diseases, limiting the ability of models to identify clinically-relevant features. We overcome this limitation by introducing a novel represen- lation learning strategy integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features. This approach enables generalization to challenging clinical tasks featuring scarce training data. We achieve this by leveraging self-supervised learning the control dataset focus inherent features that not limited to particular supervised task and incorporating meta-learning to improve the gen- eralization across domains. To explore the generalizability of the learned representations to unseen clinical applications, we apply the model to four distinct clinical datascts fcaturing scarce and helerogeneous data for neurological disorder classification. Results demonstrate the superiority our representation learning strategy on diverse clinically-relevantt tasks.
1 Introduction
Deep learning based approaches have demonstrated success in analyzing brain connectivity based on func- tional magnetic resonance imaging (fMRI) (Gadgil et al., 2020; Ahmedt-Aristizabal et al., 2021), but the scarcity and heterogeneity of fMR1 data still pose challenges in clinical applications such identifying neu- rological disorders. fMRI plays a vital rolc in identifying biomarkers for ncurological disorders (Pitsik et al., 2023; Li el al., 2021). However, clinical [MRI datasets are not only high-dimensional and spaliotemporally complex but are also characterized by high variability among subjects and a limited number data, pos- ing significant challenges for training deep learning models to predict neurological disorders (Akrami et al., 2021). Medical datasets typically contain an abundance of healthy control data but often face a scarcity of clinical data collected for any particular neurological disorder. Simply training model by aggregating

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all healthy control and clinical data may cause limited generalization and bias due to the data imbalance and heterogeneity in clinical features. This can in-turn lead to poor performance in the group with clin- ical pathology (Azizi et al., 2023). Moreover, deep learning models often struggle to achieve satisfactory performance on small-scale clinical datasets, frequently under-performing compared to traditional machine learning methods (Akrami et al., 2024; 2021). Considering the limitations of both data and existing deep learning models, we explore representation learning on fMRIs, aiming to extract meaningful and general- izable features from data by learning inherent functional activity patterns. Driven by the need to learn generalizable representations, we leverage self-supervised learning and meta-learning the foundation of our representation learning approach.
Self-supervised learning is popular in representation learning and has shown the ability to improve the generalization of features (Reed et al., 2022; You et al., 2020b). In contrast to fully-supervised tasks such as classification or segmentation, self-supervised tasks are typically designed to learn intrinsic features that are not specific a particular task (Taleb et al., 2020). Contrastive self-supervised learning applied to fMRI classification has demonstrated the ability to prevent over-fitling on small medical datasets and address high intra-class variances (Wang et al., 2022). For our proposed approach, we apply contrastive self-supervised learning, known to be effective in represcntation learning (Azizi et al., 2023), to the healthy control data to learn more generalizable features.
Now that self-supervised learning is applied to learn generalizable representations from abundant healthy control data, we need an effective approach to transfer the learned knowledge to the scarce clinical data. For this, we adopt a meca-represnntation learning approach (Liu et al., 2020a), which leverages meta-learning to enhance generalization across domains. Meta-learning has recently gained tremendous attention because mechanism, which strongly increases the generalizability of models across differ- ent tasks (Zhang et al., 2019; Liu et al., 2020a; Finn et al., 2017). By employing a bi-level optimization scheme (Finn et al., 2017), the model is trained to generalize effectively to unseen domains. Meta-learning is particularly effective in a low-data regime (Zhang et al., 2019), making it well-suited for clinical applications.
of its Maaringgto-leante
In this work, we introduce a novel representation learning strategy, Meta Transfer of Self-supervised Knowl- edge (MeTSK), which leverages meta-learning to generalize sele-supervieed features from large-scale control datascts (source domain) to scarce clinical datasots (target domain). McTSK not only enables effective knowledge transfer from source to targel domains but also enhances the model's ability to generalize lo new and unseen clinical data in challenging applications by leveraging the learned generalization from control features to scarce clinical features. In summary, our contribution is three-fold:
We are the first to propose novel representation learning approach for fMRI data to achieve generalization across various challenging neurological disorder classification tasks with limited data;
The proposed approach can serve as a reliable feature extractor for future challenging tasks with limited data, where deep learning methods typically fail.
We address the heterogeneity and scarcity of clinical fMRI data through the integration of meta- learning and self-supervised learning.
Our experiments are designed to demonstrate, i). the improved knowledge transfer from source to target datasets, we use a neurological disorder classification task to evaluate the performance on the target domain when applying McTSK in a knowledge transfer task setting. ii) The generalization of representations to unscen clinical datasots. We evaluate the McTSK model pre-trained with a source and a target dataset on unseen clinical datasets. Direct training of deep learning models on these challenging clinical datasets performed poorly due to limited training data and the heterogeneity of features, also resulting in worse performance compared to simpler machine learning classifiers. So acquiring generalizable representations is crucial for these clinical datasets. According to Kumar et al. (2022), fine-tuning can distort good pre-trained features and degrade downstream performance under large distribution shifts. Here we explored linear probing for evaluating the generalization of representations on distinct neurological disorder classification tasks. As we show below, we are able to consistently achieve superior classification performance for diverse neurological disorder identification tasks compared to linear classification directly using traditional functional connectivity features input.
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2
Related Work
EMRI data are widely used for identifying neurological disorders such as Alzheimer's Disease (AD) (LaMon- tagne et al., 2019), epilepsy (Gullapalli, 2011), Parkinson's 's Discase (PD) (Badea al., 2017), and Attention- Defici//Ilyperactiiity Disorder (ADHD) (Bellec et al., 2017). These disorders cause atypical brain activity that can be characterized by analyzing fMRI data. In traditional setting, features such as functional connectivity between brain regions (Van Den Heuvel & Pol, 2010) and Amplitude of Low-Frequency Fluctu- ation (ALFF) features (Zou et al., 2008), are extracted from raw fMRIs for statistical analysis or as inputs to machine learning classifiers to identify neurological biomarkers (Akrami et al., 2024). Given the inherent graph structure of fMRI data, where brain regions can be considered as nodes and functional connectivity measures as the edges, Graph Neural Networks (GNNs) (Li et al., 2021; Gadgil et al., 2020; Wang et al., 2022) have emerged as the predominant approaches the litcrature for deep learning. GNNs usually take graph-structured data as input and perform graph convolution based the neighboring relations (defined by edges) between graph nodes (Kipf & Welling, 2016). Li et al. (2021) proposed GNN model with novel pooling strategy for Autism Spectrum Disorder (ASD) classification; Zhang et al. (2023) applicd a local- to-global GNN to ASD and AD classification, which uses population graph to make use of inter-subject correlations in the dataset. In this work, we adopted a popular spatio-temporal GNN as our backbone model.
In fMRI analysis, hetcrogeneity and disparitics across datasets pose significant challenges for the general- ization of models. Most existing methods developed for fMRIs focus on adapting models between closely related domains, such generalizing across imaging sites within the same dataset. These methods are often designed to address site-specific variations, such as differences in imaging protocols or scanner types (Li et al., 2020; Shi et al., 2021; Liu et al., 2023). Other approaches, such as fine-tuning (Raghu et al., 2019) and multi-task learning (Huang et al., 2020), attempt to adapt to target domains but often fail to learn transfcrable features due to domain discrepancics (Liu et al., 2020b; Raghu et al., 2019) and may distort the learning of larget-domain specific features (Chen et al., 2019).
Despite these efforts, there is no existing work exploring the generalization across domains with fundamen- tally different characteristics, such as hcalthy control data and clinical data from patients with neurological disorders. To miligate this gap, we explore representation learning strategy that seeks lo enhance gen- eralization to unseen domains not available during training. This challenge, typical in real-world clinical applications (Akrami et al., 2024; Badea et al., 2017), where new datasets may vary significantly and are often scarce, makes traditional deep learning methods less applicable. Our approach also serves as a reliable feature extractor for small-scale datasets, thereby enabling robust neurological disorder identification.
3 Methods
In this section, we introduce proposed strategy, McTSK, which improves the generalization of self- supervised IMRI features from a control dataset to clinical datasets. The proposed network architecture consists of a feature extractor that learns generalizable features from both source (control) and target (clin- ical) domains, and source and target heads to learn domain-specific features for the source and target domain, respectively. The bi-level optimization strategy is applicd to Icarn gencralizable features using a Spatio-temporal Graph Convolutional Network (ST-GCN) (Gadgil et al., 2020) as the backbone model. Af- ter MeTSK is trained on the control and clinical datasets, its generalization capability will be evaluated on unseen clinical datasets using linear probing. The methodology of MeTSK as well as the representation learning pipeline are shown in Figure 1.
3.1 Feature Extractor: ST-GCN
We adopt a popular model for IMRI classification, ST-GCN (Gadgil et al., 2020), as the backbone architecture to extract graph representations from both spatial and temporal information. A graph convolution and a temporal convolution are performed one ST-GCN module shown Fig. 2, following the details in Gadgil et al. (2020). The feature extractor includes three ST-GCN modules. The target head and the source head share the same architecture, which consists of one ST-GCN module and one fully-connected layer.
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Inner Loop Update
MeTSK
Meta training
Target Domain
Target
Meta-training
Head
loss
Metaa validution
Feature Extractor
Meta-validation
loss
Source
Sell-supervised
Source Domain
Head
Outer Loop Update
Training with source and target data
fMRI Representation
Generalization Evaluation
Pre Pre-trained
Output
Feature
Extractor
Classify
Classifier
Unseen
Disorder
Clinical Dataset
Figure 1: An illustration of MeTSK for generalizable representation learning. In MeTSK, two optimization loops are involved in training. The inner loop only updates the target head, while the outer loop updates the source head and feature extractor. The representation learning pipeline involves first training MeTSK on the source and target data, and then evaluate the learned representations on unseen clinical datasets using neurological disorder classification tasks.
To construct the graph, treat brain regions parcellated by brain atlas (Glasser al., 2016) as the nodes and define edges using the functional connectivity between pairs of nodes mcasured by Pearson's correlation coefficient (Bellec et al., 2017). We randomly sample fixed-length sub-sequences from the whole fMRI time series to increase the size of training data by constructing multiple input graphs containing dynamic temporal information. For each time point in each node, a feature vector of dimension C, is learned. So for the r-th sub-sequence sample from the n-th subject, the input graph x. to the i-th layer has dimension of P x L x C, where P is the number of brain regions parcels (nodes), L is the length of the sampled sub-sequence, and Co = 1 for the initial input. In ST-GCN, graph convolution (Kipf & Welling, 2016), applied to the spatial graph at time point in the i-th layer, can be expressed as follows.
x D-11111 (0-1/((+11)D--1 - Wc,xC.4
(1)
where the adjacency matrix consisting of edge weights defined as Pearson's correlation coefficients, I is the identity matrix, D is a diagonal matrix such that D; = Ej Aij + 1, and W trainable weight matrix. We then apply ID temporal convolution to the resulting sub-sequence of features on each node. A voting strategy is applied to combine predictions generated from different sub-sequences.
3.2 Bi-level Optimization
Assune there exists source domain (healthy controls) S with abundant training data Xs and target domain (clinical) T, where the training data Xy is limited. A feature extractor f(ø), target head hr())). and source head hs,), are constructed to learn source features hs(f(Xs; (); 0,) as well as target features hy (f(:(())); where é, O,, and 0g are model parameters.
We introduce bi-level optimization strategy to perform gradient- gradient-based update of model parameters (Finn et al., 2017; Liu al., 2020a). The model first back-propagate the gradients through only the target head in several fast adaptation steps, and then prok-prooa through the source head and feature extractor. Each step in nested loop is summarized follows:
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ST-GCN Module
Temporal Convolution
Prediction
Voting
Final Prediction
Prediction
Input Subsequence
ST-GCN Module
ST-GCN Module
ST-GCN
Module
Spatial Convolution
Imput Subsequence
Figure 2: An illustration of the ST-GCN model architecture. Spatial graph convolution is first applied to the spatial graph at each time point. Then 1D temporal convolutions are performed along the resulting features on each node. Multiple sub-sequences are randomly sampled from the whole time series as input graphs.
Outer loop (M iterations): Step 1. Initialize the target head and randomly sample target meta-training set XT,, and meta-validation set Xy.,, from Xy. where XT. N = 0, XT. U XT.
Step 2. Inner loop (k update steps): Only target head parameters 0g are updated using optimization objective LT (see below) for the target task. The parameter a is the inner loop learning rate, and 0g the target head parameter the j-th update step.
05t1= = ot
(2)
Step 3: After the inner loop is finished, freeze the larget head and update feature extractor parameters & and source head parameters 0g. The target loss LT and source loss Ls are defined in the following section. The paramoter B is the outer loop learning rate, and À is a scaling coofficient.
(0:+) (tt1 10', - 3(V oz Ls(hs(((Xs:;)); + (f 0()))
The target head, source head and feature extractor updated in alternating fashion. The target head is first trained on X7, in the inner loop. In the outer loop, the feature extractor and source head are trained to minimize the generalization error of the target head on an unseen set XT,,, as well as to minimize the source loss. Tn this way, the feature extractor encodes features beneficial for both domains and the source head extracts features from the source domain that enable generalization to the target domain.
3.3 Contrastive Self-supervised Learning
To further boost the generalizability of features, we apply a graph contrastive loss (You et al., 2020a) to perform a self-supervised task on the source domain. We randomly sample sub-sequences (m.rr), X (m.r2) (rl * r2) from the whole IMRT time series for subject. n as the input graph features (Gadgil et al., 2020), which can be viewed as an augmentation of input graphs for ST-GCN. ((am) and X(m.ra) should produce similar output graph features even though they contain different temporal information. The graph contrastive loss enforces similarity between graph features extracted from the same subject and dissimilarity between graph features extracted from different subjects (Chen et al., 2020b), so that the model learns invariant functional activity patterns across different time points for the same subject and recognizes inter-subject variances. A cosine similarity is applied to measure the similarity in the latent graph feature space (You et al., 2020a).
N Ls
(x, s, n, n)/T) exp exp
N log >
Σ 1=1
(4)
Em= Imm exp (sim{ Xs, n. m)/r)
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(x((.)1))
T (
sim(X, n. m)
(5)
[ [X
where Xs = hs(s(Xs;)); is the generated graph representation, is a temperature hyper-parameter, and N is the total number of subjects in one training batch. By minimizing the graph contrastive loss the source domain, the model produces consistent graph features for the same subject and divergent graph features across different subjects, which may be related to latent functional activities that reveal individual differences. Such intrinsic features are likely to be invariant across domains, promoting better generalization to unseen data.
The optimization objective LT of the target domain depends on the target task. In classification task with class labels Yy, adopt the Cross- Etoss-Entropy loss. The total loss for the proposed McTSK strategy is:
Lmeta = Ls + XLT
CT= Σ Yy (r100(1
(6)
3.4 Linear Probing
The bi-level optimization strategy and contrastive self-supervised learning work together to learn robust, domain-invarianl features, enhancing the ability to generalize effectively to unseen clinical datasets. Here we evaluate the generalization of learned representations through linear probing, crucial method for eval- uating the quality of representations Icarned by the model (Chen et al., 2020a; Kumar et al., 2022). Lincar probing involves freezing the parameters of a pre-trained model and training a linear classifier on the out- put. The intuition behind linear probing is that good representations should be linearly separable between classes (Chen et al., 2020a). We apply the Me'TSK model pre-trained on the source and target domains to directly generate fcatures for the unseen fMRI data without any fine-tuning. We then input these features into a linear classifier to perform neurological disorder classification. In alignment with common practices in the literature (Ortega Caro et al., 2023; He et al., 2020; Grill et al., 2020), we adopted linear SVM and logistic regression probing classifiers. Both produce lincar decision boundarics, enabling evaluation of representation quality through linear separability.
4 Datasets
We use HCP (Van Essen et al., 2013) as our source dataset due to its large size. For target datasets, we use the ADHD (Bellec et al., 2017) datasets, and the ABIDE dataset. (Craddock et al., 2013) during the training of the MeTSK model. We then introduce four independent clinical datasets as held-out domains for evaluating generalization performance. For all the IMRI datasets used in this work, we parcellate the brain into 116 Regions of Interest (ROIs) using the Automated Anatomical Labeling (AAL) atlas in Tzourio- Mazoyer et al. (2002). The AAL atlas was defined based on brain anatomy. It divides the brain into 116 regions, including 90 cerebrum regions and 26 cerebellum regions. These 116 regions form the nodes of our graph. The fMRI data were reduced to a single time series per node by averaging the voxel-level signals within each region of interest (ROI). We then computed pairwise Pearson correlations between ROIs to obtain functional connectivity matrix of size 116 x 116. The upper triangular part, including the diagonal, was extracted and flattened to form the input connectivity features for machine learning classifiers.
4.1 HCP Dataset
The healthy control data (source domain) is drawn from the Human Connectome Project (HCP) S1200 dataset (Van Essen et al., 2013). The HCP database includes 1,096 young adult (ages 22-35) subjects with Mesting-state--MRR data collected at total of 1200 time-points for cach of four sessions. We used publicly available preprocessed [MRT data that follow the minimal preprocessing pipelines described in Glasser et al. (2013). Pre-processing steps include spatial distortion correction using field maps, motion correction through ridid-body registration, and EPI distortion correction. Functional images are registered each subject's structural Ti-weighted image, followed by spatial normalization Montreal Neurological Institute (MNT) space. The data are then processed to align cortical data to standard surface space (grayordinate system).

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4.2 ADHD-Peking Dataset
The Attention-Defictt Haperratiittr Disorder (ADHD-200) consortium data from the Peking site (Bel- lec et al., 2017) includes 245 subjects in total, with 102 ADHD subjects and 143 Typically Developed Controls (TDC). To model the situation where the clinical target data set is small, we use only the subset of the larger ADHD database that was collected from the Peking site. In the ablation stud- ies, we explore the impact of using the complete set. We use the preprocessed data released on (http: //preprocessed-conmee www.eetooeesprooooo........
org/adhd200/). During preproccssing, the initial steps involve discarding the first four time points, followed by slice time and motion correction. The data is then regis- tered to the MNI space, processed with band-pass filter (0.009Hz 0.08Hz), and smoothed using a 6 mm Full Width at Half Maximum (FWHM) Gaussian filter. All fMRIs have 231 time points after preprocessing.
4.3 ABIDE-UM Dataset
The Autism Brain Imaging Data Exchange I (ABIDE I) (Craddock et al., 2013) collects resting-state IMRI from 17 international sites. Similar to the ADHD dataset, we use only the subset of data from the UM site, which includes 66 subjects with Autism Spectrum Disorder (ASD) and 74 TDCs (113 males and 27 females aged between 8-29). We downloaded the data from org/abide/, where data was pre-processed using the C-PAC pre-processing pipeline (Craddock et al., 2013).
The fMRI data underwent several preprocessing steps: slice time correction, motion correction, and voxel intensity normalization. The data was then band-pass filtcred (0.01-0.1 Hz) and spatially registered to the MNI template space using a nonlinear method. Spatial smoothing was applied using a 6mm FWHM Gaussian kernel to enhance the signal-to-noise ratio. All IMRTs have 296 time points.
4.4 Post-traumatic Epilepsy Dataset
We use the Maryland Traumatic Brain Injury (TBI) MagNeTs datasct (Gullapalli, 2011) for generalization performance evaluation. All subjects suffered a traumatic brain injury. Of these we used acute-phase (within 10 days of injury) resting-state fMRI from 36 subjects who went on to develop PTE and 36 who did not (Gullapalli, 2011; Zhou et al., 2012). The dataset was collected as a part of a prospective study that includes longitudinal imaging and behavioral data from TBI patients with Glasgow Coma Scores (GCS) in the range of 3-15 (mild to severe TBI). The individual or group-wise GCS, injury mechanisms, and clinical information is not shared. The fMR1 data are available to download from FITBIR (https: //fitbr mih. gih.. In this study, we used fMRI data acquired within 10 days after injury, and scizure information was recorded using follow-up appointment questionnaires. Exclusion criteria included a history of white matter disease or neurodegenerative disorders, including multiple sclerosis, Huntington's 'ss disease, Alzheimer's disease, Pick's 's disease, and a history of stroke or brain tumors. The imaging was performed on a 3T Siemens TIM Trio scanner (Siemens Medical Solutions, Erlangen, Germany) using a 12-channel receiver-only head coil. The age range for the epilepsy group was 19-65 years (yrs) and 18-70 yrs for the non-epilepsy group.
Pre-processing of the MagNe'Ts rs-MMRI data was performed using the BrainSuite fMRI Pipeline (BFP) (https://brainsuiee) org). BFP is software workflow that processes [MRI and T1-weighted MR data using a combination of software that includes BrainSuite, AFNI, FSL, and MATLAB scripts to produce processed fMRI data represented in a common grayordinate system that contains both cortical surface vertices and subcortical volume voxels (Glasser el al., 2013). The pre-processing follow the same minimal processing pipeline used for HCP data.
4.5 Alzheimer's Disease Dataset
Open Access Series of Imaging Studies (OASIS-3) (LaMontagne et al., 2019) is longitudinal ncuroimaging, clinical, and cognitive dataset for normal aging and Alzheiner Disease (AD), including 1379 participants: 755 cognitively normal adults and 622 individuals at various stages of cognitive decline ranging in age from 42-95 years old. Resting-state fMRI data was used for the classification of Alzheimer's Discase and is publicly available https://www. http:://wwwooosiiseee casis-braing org. We randomly selected a subset of the OASIS-3 dataset, consisting of 42 subjects diagnosed as AD and 42 cognitively normal subjects for the dewnstream
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clinical task. We are using small subset to simulate the case where only limited discase data is available. There are 164 time points in each IMRT scan. The preprocessing steps used for OASIS-3 dataset are the same as used for the PTE dataset. We perform binary classification between AD and control subjects.
4.6 Parkinson's Disease Datasets
The Parkinson's Disease (PD) dataset collected by the group of Tao Wu (Badea et al., 2017) includes both T1-weighted and resting-state IMRI scans from 20 patients diagnosed with PD and 20 age-matched normal controls. All fMRI scans have 239 time points and were collected from a Siemens Magnetom Trio 3T scamnrr.
Tao Dataset:
The Neurocon dataset is provided by the Neurology Department of the University Emergency Hospital Bucharest (Romania) (Badea al., 2017), which includes 27 PD patients and 16 normal controls (with 2 replicate scans per subject). Both the rs-IMRI and T1-weighted scans were collected from a 1.5-Tesla Siemens Avanto MRI scanner. All fMRI scans have 137 time points. Both the Neurocon and TUU datasets were downloaded from sttps://fcon. c/o11000..0..oooo/o 1000. pwwperts.nnne..e.cccc.pppppoo..e nitrr. org/indi/ htttdi///rrrto/r//iooooaaa par.insons....... html. We pre-processed Tao Wu and Neurocon fMRI data using the same preprocessing pipeline (BFP) that was applied the PTE dataset.
Neurocon Dataset:
5 Experiments and Results
5.1 Evaluation of Representation Transferability
To validate the effectiveness of McTSK when training with a source and a target domain, we first evaluate the knowledge transfer from the HCP data (healthy controls) to ADHD-Peking data and ABIDE-UM data (clinical data). We designed experiments for tasks that perform ADHD v.s. TDC classification and ASD v.s TDC classification, respectively. We evaluate different strategies and compare their effectiveness in enhancing the knowledge transfer from healthy datasct (source) to a clinical datasct (target).
For comparison, we designed (i) baseline model using ST-GCN with supervised task directly trained on the target dataset (Baseline), (ii) a ST-GCN model fine-tuned on the target dataset after pre-training on HCP data (FT), (iii) a model performing multi-task learning on both source and target datasots si- multaneously (MTL), and (iv) the proposed strategy, MeTSK. We incorporated MTL. and FT methods for comparison in order to investigate whether McTSK superior traditional approaches terms of knowl- edge transfer. We compared several baseline methods: a Linear Support Vector Machine (SVM), Random Forest Classifier (RF), Multi-Layer Perceptron (MLP) consisting of three linear layers, LSTM model for [MRT analysis (Gadgil et al., 2020), and model combining a transformer and graph neural network (STAGIN) (Kim et al., 2021). For the SVM, RF, and MIP, the inputs are flattened functional connectivity features, calculated using the Pearson's 's correlation coefficient between [MRT lime-series across pairs of brain regions defined in the AAL atlas. LSTM and STAGIN use raw fMRI time-series as their input.
We use 5-fold cross- cross-validane validation to split training/ teenniggt/tttnggggeg sets on ADHD--oling/ABIDDEEE.. HCP data for training. Model performance is evaluated using the average tree--undr-the--OC-uuu shown in Table 1. MeTSK achieved the best mean AUC of 0.6981 and 0.6967 for both target datasets, which is significant improvement compared to the bascline model trained only on target data. McTSK also surpassed the performance of fine-tuning and multi-task learning. The results demonstrate that the McTSK strategy possesses the capability to enhance the knowledge transfer from healthy data to clinical data.
data and use all
(AUC)
5.2 Evaluation of Representation Generalizability
Now we have a McTSK model trained on HCP and ADHD-Peking data, we evaluate its generalization to four clinical datasets characterized by their small sample sizes and the inherent challenges they present in the identification of ncurological disorders. The generalization performance was evaluated on challenging neurological disorder classification tasks. Specifically, the PTE dataset was used for classifying PTE subjects and non-PTF subjects; the OASIS-3 dataset. was used for binary classification distinguishing Alzheimer's
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Table 1: A comparison of mean AUCs of 5-fold vross-validation on ADHD dataset and ABIDE dataset using different methods: finc-tuning, multi-task learning, the proposed strategy McTSK, and other bascline methods.
Method
Source
Target
Target-only
ADHD-Pcking
ABIDE-UM
SVM
0.6182 0.0351
0.6286 0.0635
RF
0.6117 + 0.0503
0.6266 + 0.0612
MLP
0.6203 + 0.0468
0.6312 = 0.0724
LSTM (Gadgil et al., 2020)
0.5913 + 0.0510
0.5936 + 0.0622
STAGIN (Kim et al., 2021)
X
V
0.5638 = 0.0468
0.5812 + 0.0684
ST-GCN (Baseline)
V
0.6215 + 0.0435
0.6051 + 0.0615
FT
X
0.6213 + 0.0483
0.6368 0.0454
MTL
X 0.6518 0.0428
0.6345 - 0.0663
MeTSK (ours)
X
0.6981 0.0409
0.6967 0.0568
Disease (AD) from cognitively normal individuals; for the TaoWu and Neurocon datasets, conducted binary classification to differentiate between Parkinson's Discase (PD) patients and healthy control subjects.
5.2.1 fMRR-bbased Foundation Models for Generalization Comparison
We compare our representation learning method with foundation models to assess how well our approach gencralizes to unscen clinical data relative these powerful, large-scalc models. Foundation models are designed to capture generalizable representations across wide variety of downstream tasks. By contrasting MeTSK's learned representations with those from foundation models, our method in generating robust and generalizable features that perform well even with limited clinical data.
evaluate the effectiveness of
We compared our MeTTK model to large pre-trained fMRI model, as detailed in Thomas et al. (2022). This model involves pre-training a Generative Pretrained Transformer (GPT) (Radford et al., 2019) on extensive datasets comprising 11,980 IMRI runs from 1,726 individuals across datasets. During pre-training, the GPT model performs a self-supervised task predict the next masked time point in the fMRI time-series. Their pre-trained model is publicly available at https://github. httlg//thhu..cm/aaaaaa.. wwtaa/teeaaaann--eeeeee foog-ffoombbiiin We directly applied their pre-trained model to generate features. BrainLM (Ortega Caro et al., 2023), of the latest foundation models developed for IMRI, was also incorporated into our experiments for com- parison. BrainLM, consisting of a masked auto-encoder and a vision transformer, was trained on 6,700 hours of fMRI recordings. During pre-training, BrainLM incorporates a self-supervised task that pre- dicts the randomly masked segments of time series in fMRI data, which is similar to the pre-training task Thomas et al. (2022). Similarly, we directly applied their pre-trained model available at https: //github. com/vandi,jklat /BrainLn to generate features. We also pre-trained ST-GCN model on both HCP and ADHD- Peking datasets using only the proposed contrastive self-supervised learning (SSL). From this pre-trained SSL model, we again directly generated features for unseen clinical data.
5.2.2 Evaluation Results
We compare the linear probing performance across foundation model approaches as well as the performance of classifiers trained with functional connectivity features extracted from raw fMRT data. We employed machine lcarning classificrs, including a lincar SVM, RF, and Logistic Regression (LR). The same 5-fold cross-validation was applied and AUCs for classification tasks were computed. Although complex classifiers could be used, the use of SVM, RF and LR isolates the quality of the learned representations from the model's complexity.
Our proposed MeTSK achieved the best performance on all four datasets, outperforming the two latest ÍMRI foundation models, as shown in Table The features generated by MeTSK, the SSL model and BrainLM (Ortega Caro et al., 2023) all achieved better performance than functional connectivity features, owing to the knowledge learned from their extensive pre-training datasets. However, due to the domain gap between their pre-training data and the clinical datasets used here, the foundation models may require
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0.2
0.0
Figure Feature importance map of PTE features generated from MeTSK shown color-coded ROIs overlaid the AAL atlas. The numbers represent the absolute value of coefficients from the trained SVM.
additional fine-tuning to further improve their performance. These results highlight MeTSK's potential in enhancing representation learning for clinical diagnostic purposes.
5.2.3 Interpretation of Learned Representations
To gain further insights and enhance the interpretability of the representations learned by McTSK, we computed a feature importance map based on the positive coefficients from the SVM trained for PTE prediction. In linear SVM, each feature within each ROI (brain region) is assigned coefficient, indicating its importance in the model's decision-making process. The higher the absolute value of coefficient, the greater its impact on the model's predictions. The coefficient for each ROI is calculated as the average of the coefficients for all features within that ROI. We extracted these coefficients for each ROI from the trained SVM and visualized them as a feature importance map overlaid on the brain, as shown in Fig. 3. Through observing the feature importance map, we identified that the most significant regions for PTE prediction are located in the temporal, parietal, and occipital lobes. Since epilepsy most commonly occurs the temporal lobes, it is not surprising to see that they are among the regions identified from the feature important maps. A recent study PTE (Akrami et al., 2024) also reported statistically significant differences between PTE and non-PTE groups in the parietal and occipital lobes. This alignment between interpretation and clinical evidence demonstrate that the representations Iearned by McTSK are not only predictive but also clinically meaningful.
5.3 Implementation Details
Training: To optimize model performance, we follow the training setting in (Gadgil et al., 2020) for the ST-GCN model. The length of input sub-sequences for ST-GCN is fixed at 128. We generate one meta- training batch by randomly selecting an equal number of samples from each class. The batch size is 32, both for the meta-training and the meta-validation set. We use an Adam optimizer (Kingmna & Ba, 2014) with learning rate B 0.001 in the outer loop, and an SGD optimizer (Ketkar, 2017) with learning rate a 0.01 in the inner loop. The number of inner loop update steps is 25. We set the hyper-parameter À A= 30 and the temperature parameter 2020a). Since contrastive loss converges slowly (Jaiswal et al,, 2020), warm-up phase is applied to train the model only on HCP data using the graph contrastive loss for the first 20 epochs. The number of total training epochs is 90. The hyper-parameters for the baseline ST-GCN model and the contrastive SSL loss follow the settings reported in Gadgil et al. (2020); You et al. (2020a). Other hyper-parameterd were selected empirically by observing the convergence of the training loss. The hyper-parameters SVM and RF during linear probing are selected via grid search with nested cross-validation. For the multi-tase learning (MTL) implementation, we simply remove the inner loop in McTSK and use all the training data
30 to adjust the scale of losses following (Liu et al., 2020a; You et al.,
MeTSK
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Table 2: Generalization evaluation results using 5-fold cross-validation: Mean and std of AUCs for PTE, AD, and PD classification using representations generated from different models (denoted as 'lincar probing" in the Table) as well as directly from functional connectivity features. The best performance achieved on each dataset is highlighted in bold.
Linear Probing
Connectivity Features
McTSK
SSL
Thomas al. (2022)
Ortega Caro et al. (2023)
SVM
0.6415 0.0312
0.5972
0.0492
0.5369
0.0451
0.6011
0.0465
0.5697 + 0.0477
PTE
RF
0.5392 + 0.0553
0.5253
0.0486
0.4814
0.0664
0.5589
0.0611
0,5081 + 0.0612
0.5770 +: 0.0635
0.5087
0.0455
0.5199
0.0678
0.5378 0.0663
0.5190 +: 0.0566
SVM
0.6407 = 0.0741
0.5992 = 0.0867
0.6115 = 0.0582
0.6028 0.0831
0.5541 = 0.0677
OASIS-3
RF
0.6750 + 0.0753
0.6055
0.0682
0.6233 0.0672
0.5604 0.0607
0.5329 + 0.0721
LR
0.5894 + 0.0544
0.5044 0.0581
0.5543 0.0439
0.5207 0.0437
0.5482 + 0.0520
SVM
0.6831 + 0.1431
0.6371
0,1578
0.6528 = 0,1586
0. 0.4937 1 0.1506
0.5725 + 0.1502
Thowu
RF
0.6353 + 0.1701
0.6273 = 0.1679
0. 0.4883 = 0,1885
0,4891 + 0.1710
0.6031 + 0.1631
LR
0.5725 + 0.1696
0.5100-
0.1674
0.5175 + 0.1848
0.5387 + O. 091985
0.5275 + 0.2057
SVM
0.7529 1 0.1579
0.5643
0.1068
0.6433 0.1535
0.6476 1 0,1686
0.6599 1 0,1807
Neurocou
RP
0.6230 1 0.1658
0.5219
0.1627
0.5813 0,1818
0,6774 1: 0.1676
0.5427 +: 0.1715
LR
0.5885 + 0.1815
0.5033 0.1768
0.5238 0.2001
0.5277 + 0.1911
0.5163 + 0.2167
FTE
OASIS-3
TaoWu
Naurocon
Peking
MCTSe AUMO
0.30
Connectriity Reatures
0.75
0.65
0.60
0.40
0.30
0.10
0.00
10M
18 9V76
18
Figure A comparison of the lincar probing performance between McTSK model trainod with a single larget site (MeTSK-ADHD-Peking results from Table 2) and MeTSK model trained using all the target sites from ADHD-200 dataset (MeTSK-ADHD-AII) Also shown are results from linear classifiers trained directly using connectivity features for bascline comparison. The height of cach bar indicates the average AUC computed from a 5-fold cross-validation, while the error bars denote the standard deviations. MeTSK- ADHD-AIl achieved similar performance to MeTSK-ADDD-Peking on all the four downstream datasets.
to update the target head. Both heads and the feature extractor are updated simultancously in one loop. For meta-learning, the target training set in each fold is further divided into meta-training set X of 157 subjects and a meta-validation set XT... of 39 subjects. Both the meta-train and meta-validation sets are randomly re-sampled in every iteration, and the target head is randomly initialized. In the training of MeTSK, binary classification is performed using one fully-connected layer followed by sigmoid activation. The classification loss the binary cross-cntropy loss.
For SSL and MeTSK, use the pre-trained feature extractor for generating features. The generated features are graph-level representations, having two dimensional feature matrix at each node (brain region). We averaged the features along the first dimension and applicd Pinciple Component Analysis (PCA) to reduce the dimensionality before leeding the features into classifiers. The logistic regression used in the downstream experiments consists of a linear layer and followed by sigmoid activation. The SSL model trained on both HCP and ADHD-Peking data used the same contrastive loss and hyper-paramoters in McTSK. In our comparative analysis with a foundation model for fMRI (Thomas et al., 2022), we flatten the brain signals at each time-point and input the whole time-series without. masking into the pre-trained
Evaluation:
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GPT model. This generates feature embedding for each time-point. Since there is no class token in the pre-trained model, we averaged the feature embeddings across time-points (tokens) to get the output features for downstream datasets. We follow the other detailed settings of the pre-trained GPT model in (Thomas ct al., 2022). For another foundation model BrainLM (Ortega Caro et al., 2023), we followed the instructions provided on http:://githbbb com.aaddi jttp://aadiiilaal. BallaaBBrrinMMtreee imput the fMRI time-series into the pre-trained model without masking. Then we extracted the output class token as the generated features for clinical datasets.
Specifically, we directly
Computational Cost: MeTSK introduces additional training time because there is nested optimization loop within each training iteration. Training McTSK takes approximately 6 hours on a single NVIDIA V100 GPU with 32 GB of memory. This nested gradient computation in the inner loop increases both training time and memory consumption. Larger GPU memory is recommended when scaling MeTSK to larger datasets or models. The SSL model requires about 4 hours of training using the same GPU. Baseline models such as ST-GCN, LSTM, and STAGIN typically take between 1.5 to 2 hours to train. Since we evaluate the two foundation models using pre-trained checkpoints, their inference cost is negligible. While MeTSK has higher training cost, it is one-time cost at the pretraining stage. Once trained, the representations can be reused across downstream tasks with minimal computational overhead using linear probing.
6
Ablation Study
6.1 Training with More Clinical Data
In this paper we have explored a strategy McTSK which trains a model to generalize from abundant normal features to scarce clinical features. To model this situation, we employed only a single site (Peking) from the ADHD-200 dataset to train our model. MeTSK emphasizes the model's ability to extrapolate from abundant healthy control features to scarce clinical features, equipping it with generalization capabilities for unseen real-world applications where clinical data may also be limited.
However, while McTSK is intentionally tailored for training with small-scale clinical data, we also explored the impact of using more clinical target data to train the model. We used the data from all of the sites in the ADHD-200 datasets for training. The entire dataset comprised a total of 362 ADHD subjects and 585 TDCs, all pre-processed using the same steps as described in Section 4.1.2. The same linear probing was performed to evaluate the models on the four clinical datascts: PTE, OASIS-3, Tao and Neurocon. As shown in Fig 4, MeTSK consistently demonstrated similar performance for various downstream lasks when trained using the larger clinical dataset. We computed the p-value using a paired Student's ' t-test, and showed that there is no significant difference between the downstream performance of the model trained with Peking site only ADHD-AII). This underscores the model's robustness and its capacity to achieve effective generalization without relying on a large amount of training data. In some cases, training with the entire ADHD dataset achieved worse performance. The ADHD-AIl datasct includes fMRI scans from multiple imaging sites with different acquisition protocols, which can introduce distribution shifts. This phenomenon has been studied in prior work (the data, addition dilemma) Shen et al. (2024), which highlights that adding more data from heterogeneous sources can in some cases degrade model performance due to domain shifts.
SETSSK-ADD--Pkkiin
and the model trained with the entire ADHD-206 dataset (McTSK-
6.2 Ablation Study of MeTSK
We examine the individual contributions of self-superviied learning and leta-le to the model perfor- mance both target clinical datasets (ADHD-Peking, ABIDE-UM) in this section. To explore the effect of meta-learning, we designed an experiment using only the target (clinical) datasct in meta-learning (McL). This approach involves removing the source head and the source loss during bi-level optinization. The target head is first trained on the ADHD/ABII learning to generalize on a held-out validation set in the outer loop. Our results, as shown in the last two rows of Table 3, reveal that the mean AUC improved from the baseline performance of 0.6215 to 0.6562 for ADHD classification, and from 0.6051 to 0.6675 for ASD classification without source domain knowledge.
meta-traing training set in the inner loop, followed by feature extractor
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Table 3: Ablation study on ADHD-Peking and ABIDE UM dataset. The FT, MTL, and McTSK methods are compared for two cases transferring features from (i) sclf-supervised source task and (ii) sex classification source task, respectively. The last two rows are models trained only target clinical data: a meta-learning model without source task and a baseline model ST-GCN.
Datasct
ADHD-Pokeee
ABIDE-UM
Source Task
Sellssupervisin
Sex Classification
Sel-suuervvsion
Sex Classification
FT
0.6213 + 0.0483
0.6150 + 0.0497
0.6368 + 0.0454
0.6071 + 0.0742
MTL
0.6518 + 0.0428
0.6377 + 0.0512
0.6345 + 0.0663
0.6240 = 0.0711
MeTSK
0.6981 + 0.0409
0.6732 + 0.0579
0.6967 + 0.0568
0.6786 + 0.0749
MeL
0.6562
0.0489
0.6675 + 0.0505
ST-GCN (Baseline)
0.6215 0.0435
0.6051 + 0.0615
To assess the contribution of self-supervised learning, we compared the impact of using a self-supervised task versus a scx classification task on the HCP datasct. Finc-tuning, multi-task learning, and McTSK were implemented using sex classification (female vs male) as the source lask. The same 5-fold cross-validatinn method was applied to compare the average AUC, As detailed in Table 3, all three methods: FT, MTL, and McTSK, showed a degraded performance when transferring knowledge from the sex classification task. This suggests that the sex-related features of the brain may be less relevant to ADHD/ASD classification, negatively affecting the model's performance.
7 Discussion
Our proposed method, MeTSK,, addresses the critical need for generalizable representations in clinical fMRI applications, particularly in scenarios with data scarcity and heterogencity. Traditional doep lcarning ap- proaches often struggle in these scenarios due to the lack of sufficient labeled data and high variability across subjects, as shown in our results (Appendix A.1) where simple machine learning classifiers, like SVM and RF, outperformed deep learning models when trained directly on limited clinical data. The high-dimensional nature and complex spatial-tempores dynamics of fMRI data make it even more challenging to extract di- agnostic features given limited training data. These challenges underscore the importance of developing a generalizable representation learning strategy for clinical applications.
In this work, we primarily focus on evaluating representation generalization in low-data regimes. Although some datasets, such as OASTS-3, contain a large number of subjects, we only used a subset of the entire dataset and applied 5-fold valssvaali protocol to maintain consistency across all datasets. Cross- validation provides balanced estimate of generalization performance across multiple folds and ensures the evaluation setup is consistent across datasets, including those with naturally limited sample sizes. Although training on small subset and evaluating on the entire dataset is interesting to explore, it remains challenging because the limited training subjects may not capture the full variability of the entire population.
Further explorations exist for expanding this approach in future work. One direction is to experiment with alternative functional brain atlases, such as DiFuMO (Dadi et al., 2020), or incorporate different functional connectivity measures (e.g. Partial correlation) beyond Pearson's 's correlation (Varoquaux & Craddock, 2013; Smith et al., 2011). Note that our current approach is compatible with various choices of atlases and connectivity measures, which opens the potential to extend MeTSK to more advanced connectivity metrics and to casily adapt it to different atlas configurations. Additionally, exploring multi-class classification for distinguishing between multiple neurological disorders in downstream tasks hather than a simple binary classification could potentially provide further insights.
8 Conclusion
To summarize, we developed a representation learning strategy for ncurological disorders, which achieved superior generalization to diverse small-scale clinical datasets and significantly improved the performance various challenging neurological disorder classification tasks. By employing meta-learning we enhance
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the generalization capabilities of self-supervised features from the source domain (control) to target clinical domains, ensuring that the learned representations capture intrinsic pat terns in functional brain activity and such patterns are shared across datasets. The learned representations enable effective classification even with simple linear classifiers. This demonstrates the quality of the learned features and their generalizability across unseen clinical datasets.
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Table Mean and std of AUCs of classification results using 5-fold cross-validation
Methods
PTE
OASTS-3
TaoWu
Neurocon
SVM
0.5697 + 0.0477
0.5541
0.0677
0.5725 + 0.1502
0.6599 + 0.1807
RF
0.5081 + 0.0612
0.5329
0.0721
0.6031 + 0.1631
0.5427 + 0.1715
MIP
0.5111 = 0.0402
0.5231
0.0689
0.5875 0.1631
0.5313 = 0.1721
ST-GCN (Gadgil et al., 2020)
0.5108 + 0.0718
0.5079
0.0833
0.5377 0.1739
0.5089 + 0.1890
LSTM (Gadgil et al., 2020)
0.5031 = 0.0712
0.4675
0.0880
0.5019 + 0.1721
0.4914 = 0.1799
STAGIN (Kim et al., 2021)
0.4517 + 0.0806
0.4891 = 0.0904
0.4332 + 0.1859
0.4470 + 0.1822
Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge, Jiayu Zhou, and Fei Wang. Metapred: Meta-learning for clinical risk prodiction with limited patient electronic health records. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2487 2487-2495, 2019.
Yongxia Zhou, Michael Milham, Yvonne W Lui, Laura Miles, Joseph Reaume, Daniel K Sodickson, Robert Grossman, and Yulin Ge. Default-mode network disruption in mild traumatic brain injury. Radiology, 265 (3):882, 2012.
Qi-Hong Zou, Chao-Zhe Zhu, Yihong Yang, Xi-Nian Zuo, Xiang- Yu Long, Qing-Jiu Cao, Yu-Feng Wang, and Yu-Feng Zang. An improved approach to delection of amplitude low-frequency fluctuation (alff) for resting-state firri: fractional alff. Journal of neuroscience methods, 172(1):137- 141, 2008.
A Appendix
A.1 Applying Deep Learning Models to Small-Scale Clinical Datasets
We present the classification results on the clinical datasets mentioned in Section 5.2 using some popular deep learning models. These models demonstrated severe over-fitting and poor generalization, achieving lower AUCs than traditional machine learning classifiers. The deep learning models struggled to learn from the limited and highly variable clinical data, where the high- nigh-dimensional and complex nature of fMRI data and small sample sizes amplified the over-fitting issues. These results shown in Table 4 emphasized our motivation for developing a representation learning approach capable generating generalizable features for such challenging datasets.
Due methodological and experiment setting differences across previous works (e.g., varying cross-validation splits, fMRI atlas choices, and number of training samples used), direct numerical comparisons should be interpreted cautiously. For example, different IMRT atlases define varying numbers of ROIs, which directly impact the dimensionality of the input connectivity features and change the number of nodes in the input graphs. We include these published results from previous works to contextualize our results within the broader literature, as shown Table 5. Unlike previous works that often leverage larger datasets, our experimental design simulates real-world clinical scenarios with limited training data. This allows us assess the robustness and generalization capability of MeTSK under data-scarce conditions settings that are common in clinical practice.
A.2 P-values Computed from Wilconxon Signed-Ram Rank Tests
We report the p-values computed using the Wilcoxon signed-rank (est between the results from Connec- tivity Features, ADHD--,, and MeTSK- MeTSK-ADHD-PEH ADHD-Pekingg Table 6. As shown, the comparisons between MeTSK-ADHD-Pokim and McTSK-ADHD-AI consistently yield high p-values, indicating no sta- tistically significant difference between them. In contrast, comparisons between McTSK-ADHD-Pede Connectivity Features result in smaller p-values, suggesting more substantial differences in performance.
and
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Table 5: Summary of reported results on our four climical datasets in the literature as well the best results achieved by McTSK. Note the significant differences in metrics, cross-validation splits, training size, and
[RR atlases.
Dataset
Method
Results
Motric
CV Splits
Training Samples
IMRI Atlns
ROIs
(2021)
Accuracy
(Schaefer et 2018) Bramnetome (Fan et al., 2016)
Shi al. (2022)
0.667
AUC
10-6ld CV
42
246
Neurocom
Vigueswwaran (2024)
0.5
F1 Score
u0% for testing
Haroard Oxford
69
al. (2021)
Accurney
McTSK (Ours)
0.7529
AUC
d.lad CV
42
AAL
116
al. (2024)
0.775
Accuracy
10-fal CV
Schoefer
Thown
La nl. (2021)
Vignsswwaaan et aL. (2024)
0,875
Accuracy
Store
10-fald CV
10% for testing
40
Harward Oxford Atlas (Wi al., 2018)
Schnefer
100
69
MeTSK (Ours)
AUC
6-aall
AAL
Ham al. (2024) (Propesed GCN)
0.800
Accuracy
10 Anla CV
1006
Scharder
100
OASIS-3
Hom al. (2004) (Boelliee GUN)
(2024) (SVM)
0.686
Accuracy
Accuracy
10-flli CV
10df
1006
Schoeker
Schaeder
100
McTSK (Ours)
0.675
AUC
G-old CV
84
AAL
116
Akramii (2024) (KSVM)
0.6100
AUC
Lauve mawve-oee-un
CV
USCLober (Joshi al., 2022)
PTE
Alrami (2024) (GCN)
0.5000
Leeve-ore-oot
USC
McTSK (Ours)
0.6:15
AUC
5 fuld CV
72
AAL
116
Table 6: Wilcoxon signed-rank test p-values between MeTSK and two other methods in Figure 4 across all datasets and classifiers. Columns represent comparisons against McTSK-ADHD-Poking using McTSK- ADHD-All and Connectivity Features, respectively. A higher p-value indicates a less significant difference between methods.
Datasct
Classifier
vs ADHD-AIl
vs Connectivity Features
SVM
0.4375
0.3125
PTE
RF
0.8125
0.4375
LR
1.0
0.3125
SVM
0.625
0.1250
OASIS-3
RF
0.8125
0.1250
LR
0,8125
0.3125
SVM
0.8125
0.1250
TaoWu
RF
0.8125
0.3125
LR
0.625
0.3125
SVM
0.8125
0.3125
Neurocon
RF
0.4375
0.3125
LR
0.675
0.3125
A.3 Convergence Analysis of MeTSK
To monitor the convergence and stability of McTSK training, track both the self-supervised loss and the cross-entropy loss computed from the frozen target head's predictions using the meta- velta-valiation set in Step 3 (refer to Method Section 3.2). We plot the evolution of the two losses over training iterations in Figure 5. During training, we observe that the self-supervised loss decreases well, while the meta-validation cross-entropy (classification) loss also shows a decreasing trend overall, although with some fluctuations. Fluctuations are expected, as the loss computed on a small (39-subject) randomly sampled meta-validation set. Both the meta-train and nota-validation sets are re-sampled in each training itcration, and the target classification head is randomly initialized, introducing further variability in the observed loss values. This indicates that the model is able to jointly optimize the self-supervised and classification losses in Step and that the convergence is not negatively affected by the frozen target head trained in Step 2.
The classification loss on the meta-training set after inner-loop updates in Slep 2 consistently decreases to a stable range across training epochs, as shown in Figure 5. Additionally, the final meta-training classification accuracies after inner-loop updates fall within a stable range of 72% to 81%. This suggests that the target head can quickly adapt the updated features and the optimization steps in Step do not negatively affect the convergence of the inner-loop updates in Step 2.
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NetaiTrttinng (End Inner Upddate)
Step Selluueestedd
Mera-laliddttin Loss
0.382
MM
0.37
0.514
0.31
068
0.344
18 Spoch
Opec1
Epech
Figure 5: Loss evolution during MeTSK training over 90 epochs. The first 20 epochs is warm-up phase where only the self-supervised task is trained. All loss values are averaged batches and recorded every 2 epochs. Left: Classification loss on the meta-training set after the inmer-loop updates in Step 2. We record the loss at the last update step in each training iteration. This loss remains within stable range and gradually decreases, indicating that the target head effectively adapts using features trained in Step 3 without negative interference. Middle: Self-supervised loss from Step 3, showing a steady decline. Right: Meta-validation classification loss Step 3, computed from the frozen target head, exhibits a generally decreasing trend with some fluctuations.
Comparison of linear probing performance from foundation models Thomas et al. (2022); Or- tega Caro et al. (2023) and their fine-tuned versions on four clinical datasets using 5-fold cross-validation. We directly used results of the foundation model w/o finetuning from Table 2 for comparison. Mean + std AUCs are reported for both pre-trained and fine-tuned models. Fine-tuned results are obtained by further training the foundation models on the ADHD-Peking dataset.
Table 7:
Dataset
Classifier
Thomas al. (2022) Fine-tuned
Ortega Caro et al. (2023)
Fine-tuned
SVM
0.5369 + 0.0451
0.5291 + 0.0473
0.6011 + 0.0465
0.6102 + 0.0498
PTE
RF
0.4814 + 0.0664
0.4795 0.0651
0.5589 0.0611
0.3502 0.0584
LR
0.5199 + 0.0678
0.5252 + 0.0662
0.5378 + 0.0663
0.5415 + 0.0691
SVM
0.6115 0.0582
0.6152 0.0603
0.6028 0.0831
0.5955 0.0792
OASIS-3
RF
0.6233 = 0.0672
0.6184 + 0.0660
0.5604 + 0.0607
0.5601 + 0.0622
LR
0.5543 + 0.0439
0.5482 + 0.0455
0.5207 + 0.0437
0.5280 + 0.0411
SVM
0.5528 + 0. 1,1586
0.5477 0.1602
0.4937 + O. 1.1596
0.4913 + 0.1530
TaoWu
RF
0.4843 = 0.1885
0.4782 + 0.1903
0.4891 + 0.1710
0.4965 + 0.1684
0.5175 = 0.1848
0.5083 = 0.1822
0.5387 0.1985
0.5280 = 0.1971
SVM
0,6433 = 0.1535
0.6382 = 0.1510
0,6476 : 0.1666
0,6488 : 0.1755
Neurocon
RF
0.5813 = 0.1818
0.5755 0.1799
0.6774 + 0.1676
0.6703 + 0.1728
LR
0.5238 + 0.2001
0.5104 = 0.2153
0.5277 + 0.1911
0.5292 + 0.1984
A.4 Linear Probing Results of Fine-tuned Foundation Models
To ensure a fair comparison, we fine-tuned the baseline foundation models on the same dataset HCP combined with ADHD-Poking-than was used for training MeTSK. Notably, the pre-training datasots for these foundation models already included the HCP data, so aligning the training data required only fine- tuming the ADHD-Peking dataset. Specifically, we load the pre-trained foundation models and fine-tune them on the ADHD-Peking dataset using the same self-supervised pre-training objective originally used to masked time points. After fine-tuning on ADHD-Poking, we performed
train the linear probing on the four clinical datasets. Our results in Table 7 show that the linear probing performance of the fine-tuned foundation models similar to or slightly worse than without finetuning. This may be due the large number of parameters in the foundation models (c.g., BrainLM has 13 million parameters). which makes them more prone overfitting when fine-tuned on the relatively small ADHD-Peking dataset (245 subjects versus over 40,000 in the pre-training datasets of baseline foundation models).
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Table & Summary of additional target datasets, which are different imaging sites from the same cohort ADHD/ABIDE.
Datasct
Total Subjects
Healthy Controls
Condition Subjects
Time Length
ADHD-NYU
216
98
118 ADHD
172
ADHD-NI
48
23
25 ADHD
231
ABIDE-NYU
175
100
75 ASD
176
ABIDE-Leuven
63
34
29 ASD
246
Table 9: Performance on additional sites from ADHD and ABIDE dataset (complimentary for Section 5.1).
Dataset
ADHD
ABIDE
Site
NYU
NT
NYU
Leuven
SVM
0.6202 + 0.0662
0.6887 + 0.0476
0.6944 = 0.0755
0.6672 4 0.0883
RF
0.5605 = 0.0586
0.6631 + 0.0591
0.6785 = 0.0739
0.6228 + 0.0579
ST-GCN
0.5722 + 0.0694
0.6013 + 0.0781
0.6889 + 0.0621
0.6786 + 0.0734
MeTSK
0.6704 + 0.0782
0.8020 + 0.0914
0.7268 + 0.0687
0.7116 + 0.0502
Table 10: A comparison of MeTSK models pre-trained with different datasets as the target domain. Linear probing was performed for evaluation of models on the four clinical datasets.
ABIDE NYU
Comeetivity Features
Peking [proposed)
NYU
NI
UM
Lenven
SVM
0.6415 = 0.0312
1.5779=0.00
0,5589 1 0.0392
0.5753
= 0.0159
0.5661 = 0.008
0.5928 = 0.6475
0.5697 = 0.00177
PTE
RF
0,5392 0.055M
0.5395
0.0492
0.0308
0.5156 0.0617
0.5838
0.0558
0.5783
0.5081 0.0662
MLP
0.5813 = 0.0501
0,5283
0.0397
0.54 + 0.0517
0.5038 = 0.0178
0.3169 = 0.0527
0.5592 # 0.0631
0,5111
0.0
SVM
0.6407 + 0.0741
0.0000
0.0812
0.6255 + 0.0806
0.6340 0.0889
0.6567 0.1133
0.6503 + 0.1097
0.5641 0.0667
OASIS-3
RF
0.6750 0.0753
0.5812
0.0711
0.0745
0.6233 0.0769
0.6068 0.1019
0.5906 0.1083
0.5329
0.0721
MLP
0.8034
+ 0.0725
0.0791 0.5325
0,5563 + 0.0762
0.5998 + 0.0824
0.5277 + 0.0819
0.5166 + 0.0720
0.5231
0.0680
SVM
0.6831
0.1431
0.5597
0.1419
0,6807 1: 0.1597
0.6281 1 0.1768
0.6073 1. 0.1750
0.6219 1: 0.1476
0.5725
0.1502
TaoWa
RF
0.1701
0.5375
0.1655
0.1821
0.6328 0.1642
0.3525 0.1725
0.5810 0.1651
0.6031 0.1631
MLP
0.6208
= 0,1582
1518 0.5625
0.5825 4: 0.1577
0.5750 :: 0.1521
0,3023 + 0.1583
0.6050 +: 0.1422
0.5875
0. 1631
SVM
0.7529
0.1579
0.6791
0.1772
0.6721 = 0.18-12
0.6871 0.1717
0.6943 = 0.155
0.6700
= 0.1859
0.6599 0.1807
Neurocon
RF
0.6230
0.1658
0.5883
1807
0.5756 + 0.1719
0.5850
0.1826
0.6084
1819
0.5893
0.1551
0.5427
0.1715
MLP
0.6100
= 0.1663
0.5202 0.1373
0.5478 + 0,1683
0.5756 = 0.1709
0.5497 = 0.157
0.5344 = 0.1681
0.5313 = 0.1721
A.5 Experiment Results Using Additional Target Datasets
We present additional results by training the MeTSK model with different target datasets and subsequently evaluating its generalization performance on the same clinical tasks described in Section 5.2. Table reports the knowledge transfer performance when evaluated on additional target datasots (other sites from the ADHD and ABIDE datasets). Table 10 shows the generalization performance four unseen clinical datasets using MеTSK models pre-trained on different target datasets. The results consistently demonstrate performance improvements compared to using functional conncetivity fcatures, highlighting the robustness of the proposed representation learning strategy, which further validates the effectiveness of McTSK in extracting transferable and generalizable representations across different clinical applications.
A.6 Domain Similarity
To evaluate the transferability of learned features, we mcasure the distance between features extracted from different domains using Domain Similarity (Cui et al., 2018; Oh et al., 2022). We first compute the Earth Mover's Distance (EMD) (Yu & Herman, 2005), which is basod on the solution to the Monge-Kantorovih problem (Rachev, 1985), to measure the cost of transferring features from the source to larget domain.
We define Xs- = Flatten( N Σ Xs), Xy = Flatten( -
N Em=1x as the flattened vectors of the output graph features averaged over all subjects, and then define B, and B, as the set of bins in the histograms representing feature distribution in Xs and X,, respectively. A larger Domain Similarity indicates better
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transferability from the source domain to the target domain because the amount of work needed to transform source features into target features is smaller. Domain similarity (DS) is defined as:
DS exp (-7 =
EX XT))
(7)
Σ i= 2) )= figdi
1.B.1
EMD(Xs; XT)
Σ
fiid
s.t.
Jij 2 0,
Bel
Xs B,(i)
Σ fig
j=1
Xss
(8)
[B.
Xy E B((i)l
Σ
fy
[XT
[B.
Bd
Σ
fg =1
where (i) is the i-th bin of the histogram and [B.] is the total number of bins, IXs E B.(i) is the number of features in B,))), [Xsl is the total number of features, dup is the Euclidean distance between the averaged features in (1) and B,(i), fty is the optimal flow for transforming (i) into Bı(j) that minimizes the EMD. Following the setting in (Cui et al., 2018), we set = 0.01.
A.6.1 Experiments Using Domain Similarity
To further investigate the transferability enabled by McTSK, domain similarity was computed to evaluate the knowledge transfer from control data (source) to clinical data (target) as well as from the training set to the testing set of target data. We conducted domain similarity analysis on both ADHD- and ABIDE-UM datasets to further validate the robustness and versatility of McTSK. Fig. illustrates that the sclf-supervised source fcatures have a higher similarity with the target fcatures, indicating better inter- domain transferability and thus improved performance the target classification task. Moreover, compared to the baseline, both Autra-ADDD-class// Lawi--ccassintra--SD--... ASD-caass and intra-TDC-class domain similarities between the training and testing sets of ADHD/ABIDE data are increased by McL. This enhancement provides evidence to explain the improved classification performance on training with only larget data achieved by meta-learning. By applying meta-learning, not only the inter-domain generalization of features is boosted, but also the effect heterogeneous data within the same domain is alleviated.
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Baseline
HCP-CLS
Baseline
Features
ASD: DS-0.654
DS=0.563
DS-0
ADHD: DS=0.767
CLS task
ADHD-CLS
Train
lest
Train
Test
TDC: DS-0.629
Baseline
TDC: DS-0.782
MeL
ABIDE-
CLS
SSI,
ADHD-CLS Features
MeL
Features
DS=0. 751
DS=0.595
ASD: DS-0.988
HCP-SSL
ADHD: DS=0.993
ABIDE-CLS
Train
ABIDE-CI CLS
Test
Features
ADHD-CLS
Train
ADHD-CLL
Test
TDC: DS-0.974
TDC: DS=0.991
Figure 6: A comparison of the domain similarity between HCP self-superviced features (HCP-SSL, from Baseline ST-GCN trained HCP data with self-supervised task) and ADHD/ASI classification features (ADHD- CLS, ABIDE-CLE CLS, from Baseline trained using all tween HCP sex classification features (HCP-CLS, from Baseline trained on HCP data with sex classification task) and ADHD-CLS ABIDE-CLS, the intra-class (ADHD; TDC and ASD; TDC) domain similaritics be- tween training and lesting set of ADHD/ ASD data from Baseline and MeL (a mela-learning model trained only on target data), respectively.
ABIDE data), the domain similarity be-
23


Reviews:
[{"review_id": "lkwhh9hxeZ", "reviewer": "Reviewer_qJ4R", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n- The method evaluation analysis is comprehensive. The authors have included a wide range of baseline models, datasets, as well as training configurations (direct training vs fine-tuning vs self-supervised).\n- Overall, the paper is well-written and easy to follow.\n\nWeaknesses\n- I don’t have any major concerns with the paper but I have some minor questions and suggestions, please see below.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper aims to improve the generalizability of deep learning models of neurological disorder classification in fMRI data. To mitigate the challenge of scarcity in clinical data, the authors proposed a meta-learning and self-supervised learning framework to learn generalizable representations from fMRI data of patients and healthy subjects. The authors showed that their method outperforms baseline models and previously published methods in both direct training and transfer learning setups. Moreover, they compared their method against two publicly available fMRI foundation models on four different clinical fMRI datasets. They showed that the model achieved better performance in disease vs healthy control classification.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Rather than treating the 4 clinical datasets (i.e. results in Table 2) as 4 separate binary disorder/disease vs control classifications, have the authors experimented with treating the 4 datasets as a 5-class classification instead? It would be interesting to see how MeTSK performs against the two fMRI foundation models since all healthy control data from the 4 datasets would be combined into a single class and models pre-trained on large amounts of data might be beneficial.\n- Can the authors show the statistical test result of each pair of bars in Figure 4? The error bars show there is a decent amount of overlap against the baseline connectivity features (i.e. yellow bars) too. \n- Following the previous comment, the authors mentioned there is no significant difference in performance between the model trained on all data (`ADHD-All`) vs data from a single site (`ADHD-Peking`). Following the recent approach of foundation models, one would expect the model to improve by increasing the training sample size. Figure 4 shows that the performance of MeTSK does not necessarily scale with training data. I wonder if the authors can comment on whether or not this is a desirable behaviour. \n- Can the authors briefly explain how the hyperparameters were selected? Are the hyperparameters presented in Appendix A.3 selected via an optimization process (e.g. grid search, bayesian search, etc.) or are they handpicked?\n- Minor typos and `\\citep` vs `\\citet`. e.g. Page 2 According to Kumar el. al., (2022), Page 3 end of the second paragraph, Page 9 first second of the third paragraph, etc.", "broader_impact_concerns": "I do not believe this work requires a broader impact statement.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "rEkhmZqor1", "reviewer": "Reviewer_ioAF", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper has a clear motivation and a fairly straightforward approach to tackle the proposed problem.\n\nWriting is  understandable and figures seem clean to me.\n\nI find the proposed evaluation a bit strange with SVM/RF/MLP, it would seem more straightforward to always just try actual linear classifier? especially if you call it linear probing and train for it to be linearly seperable in the meta-training stage. Also I would have also expected to see performance of ST-GCN trained from scratch on each dataset, as well as results directly taken from the published literature.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The work presents a deep learning approach for more robust medical predictions (like Alzheimers or ADHD classification) from fMRI. Bi-level Optimization (similar to MAML) learns generic features and task-specific heads are then used to classify fMRI from different datasets on top of them. Additionally, a self-supervised contrastive loss is used to further train the features to be more robust and also allow learning from unlabeled data. As a feature extractor for fMRI,a Spatio-temporal Graph Convolutional Network is used. The model is evaluated on unseen clinical datasets with different clinical tasks.  SVMs, Random Forests and MLPs are used as classifiers on top of the learned features. Results show that the proposed approach yields features which lead to better accuracies than features learned by some other approaches.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "For downstream evaluation, please also add :\n\n* result of linear classifier trained as it was trained during meta training stage (e.g., instead of linear SVM)\n* results of ST-GCN trained from scratch \n* results from published literature wherever available for the datasets", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "MHRom6JzZ0", "reviewer": "Reviewer_hBfH", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths:\n\n- The proposed method is interesting and practical and shows promising effectiveness. Especially, the integration of self-supervised learning and meta-learning is well-motivated and addresses the challenge of adapting fMRI representations to clinical data.\n- The evaluation is thorough, with comparisons across multiple baselines and datasets.\n- The paper is well-written and easy to follow.\n\n-----\n\n### Weaknesses:\n\n- MeTSK contains a multi-objective loss that simultaneously optimizes two distinct functions—self-supervised InfoNCE loss for the source domain (healthy controls) and supervised cross-entropy loss for the target domain (clinical data). Given that these losses differ in both type and scale, this raises concerns about training stability and convergence. How do the authors monitor convergence to ensure balanced optimization of both objectives? Additionally, when updating \\( \\theta_t \\) in Step 2, how is it ensured that this update does not adversely affect the optimization process in Step 3? Similarly, when updating \\( \\phi \\) in Step 3, how do the authors prevent it from negatively impacting the update in Step 2? Providing a formal convergence guarantee or an empirical analysis of training stability is required for the claims in the paper. \n- As the authors also noted, \"due to the domain gap between their pre-training data and the clinical datasets used here, the foundation models may require additional fine-tuning to further improve their performance.\", it raises concerns regarding the generalizability evaluation of the proposed method. Specifically, the observed superior generalization performance of MeTSK could be attributed either to the method itself or to the data used for pre-training. To ensure a fair comparison, either (1) the baseline foundation models should be fine-tuned on similar pre-training data, or (2) the pre-training dataset for MeTSK should align with the data used in the baselines. Without this, it remains unclear whether the advantage comes from the methodology or the pre-training data. \n- In Figure 3, why does training on ADHD-All perform worse than ADHD-Peking alone in the PTE case? Is this due to the domain shift between different sites?\n- It is important to provide source code for reproducibility of the results. \n- Text at some parts should be improved. For example, some captions are incomplete and should be revised to clearly convey their message and match the titles. In Table 3, the definition of the baseline model should be explicitly clarified both in the text and caption. In Section 5.1, it is not mentioned what classification is used for MeTSK. If it is mentioned before, please remind the reader. At the end of Section 4.5, is it PD or AD?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses the poor generalization of deep learning models in neurological disorder classification due to the scarcity and heterogeneity of clinical fMRI data, while healthy control data is abundant. To tackle this problem, they propose MeTSK, a representation learning strategy that integrates self-supervised learning with meta-learning. The problem is formulated as a domain adaptation task, where the goal is to generalize from a source domain (healthy controls) to a target domain (clinical data). Their algorithm consists of two steps: (1) contrastive self-supervised learning applied to healthy control data to learn generalizable representations, and (2) bi-level meta-learning used to optimize feature transfer from the source to the target domain. The inner loop adapts the model to the clinical domain, while the outer loop ensures that the learned features remain generalizable.\nThe authors evaluated their method on four clinical fMRI datasets, including ADHD, Autism Spectrum Disorder (ASD), Post-Traumatic Epilepsy (PTE), Alzheimer’s Disease (AD), and Parkinson’s Disease (PD). They compared MeTSK to multiple baseline methods, including fine-tuning, multi-task learning, and foundation models. Results showed MeTSK outperforms other approaches in classifying neurological disorders from limited clinical data.\nIn their ablation study, they investigated the individual contributions of meta-learning and self-supervised learning. They trained a version of the model using only meta-learning without self-supervised learning and another using self-supervised learning without meta-learning. They also test the impact of using different source tasks (e.g., sex classification instead of self-supervised learning). Their results confirmed that both self-supervised learning and meta-learning contribute to performance, and transferring from task-agnostic self-supervised features is more effective than transferring from sex-based classification.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "### Major comments:\n\n- MeTSK simultaneously optimizes self-supervised InfoNCE loss and supervised cross-entropy loss, which differ in type and scale. A formal convergence guarantee or empirical analysis of training stability is required. Clarify how convergence is monitored and how updates in Step 2-\\theta_t and Step 3-\\phi do not negatively interfere with each other.\n\n- The generalization performance of MeTSK may stem from either the method or the pre-training data. To ensure a fair comparison, it is required to either fine-tune baseline foundation models on similar pre-training data, or align MeTSK's pre-training dataset with the baseline models. Without this, it remains unclear whether the improvement comes from methodology or data differences.\n\n- Providing source code is required to validate results and ensure reproducibility. Also implementation details are totally missed in the paper.\n\n### Minor comments:\n\n- Clarification of ADHD-All vs. ADHD-Peking performance in Figure 3, PTE case\n\n- Improvements in Text and Captions:\n* Some captions are incomplete and should be revised for clarity.\n* Table 3: Clearly define the baseline model in both the body-text and caption.\n* Section 5.1: Specify what classification method is used for MeTSK (or provide a reference if mentioned earlier).\n* Section 4.5 (End): PD or AD?", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "OcOkjStcAL", "reviewer": "Reviewer_naZS", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "# strengths\n\nThe scarcity of clinical data is an important challenge.\nLeveraging more abundant healthy control data is a sound idea to address it, and has received much attention over the years, but how to do this remains an open question and the authors contribute to that search by applying methods from the recent machine-learning literature.\n\nOverall the paper is well written and illustrated, the diagrams help understand the proposed method.\n\nThe model yields better predictive performance than several baselines.\n\nSeveral independent datasets are used for evaluation.\n\n## weaknesses\n\nIf I understand correctly, the authors perform a Student t-test on cross-validation scores (with overlapping folds). Those scores are not independent, violating a crucial hypothesis of the test and invalidating its results. If I misunderstood, please provide more details on what test was performed. And if I did understand and the t-test was done on non-iid samples please remove the claims of statistical significance.\nAs several independent datasets are available, perhaps this could be leveraged for a global comparison of methods instead:\n\nDemšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine learning research, 7, 1-30.\n\nMore details should be provided about the fMRI preprocessing and construction of the connectivity matrices.\n\nPearson correlation is probably not the best way to build the connectome, at least for the baseline classifiers that use these features directly. See for example\n\nStephen M. Smith, Karla L. Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F. Beckmann, Thomas E. Nichols, Joseph D. Ramsey, and Mark W. Woolrich. Network modelling methods for FMRI. NeuroImage,\n\nGaël Varoquaux and R. Cameron Craddock. Learning and comparing functional connectomes across subjects. NeuroImage, 80:405–415, 2013. Mapping the Connectome.\n\nMoreover, a set of learn functional regions (eg the DiFuMO atlas) may yield better performance than the AAL atlas regions. Therefore the baseline might be a bit weaker than the state of the art of \"simple\" methods.\n\nThe clinical relevance of the evaluation tasks could be discussed more. Classifying Alzheimer or PD patients vs controls can usually be done more reliably with structural images; the main motivation to tackle such tasks with fMRI is often the hope to provide insights into the disease or its evolution by inspecting the model but this could be hindered by the increased complexity of the feature extraction process.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors consider the task of training classifiers to distinguish between neurological disorder patients and healthy controls, using fMRI scans.\nThey propose a method to overcome the scarcity of patient data by training a feature extractor with self-supervised learning on scans from healthy controls, which are more easily available.\nThis approach yields better results than several baselines on 4 independent datasets.\n\nThe input features are connectivity matrices between regions of the AAL atlas computed from resting-state fMRI.\nThe self-supervised task is contrastive learning; 2 (time series of) connectomes should be more similar if they are from the same subject than if they are from 2 different subjects.\nThe whole model is trained by optimizing both the performance on this self-supervised task and on the target classification task.\nThis is done by alternating between 2 steps.\nIn the \"inner\" loop, the feature extraction parameters are frozen and a \"target head\" is trained to perform the classification task, using the extracted features.\nThen in the \"outer\" loop, the target head is frozen, and the feature extraction parameters are updated to optimize a combination of the performance on the self-supervised contrastive learning task and of the performance of the (frozen) target head applied to the extracted features (on an unseen sample, ie a sample that was not used to train the target head).\n\nHCP is used as the source dataset (for the self-supervised training), and ADHD and ABIDE are used as target datasets (to which the target head is applied during training).\n\nOnce trained, the model is evaluated on four independent datasets: MagNeTs, OASIS, TaoWu, Neurocon.\nThe evaluation is done by using the extracted features as inputs to a linear classifier and measuring its area under the ROC curve with 5-fold cross-validation.\nThe proposed method outperforms several baselines in this setting.\nOne of the baselines is the more traditional approach of training a classifier on the flattened connectivity matrix extracted from fMRI time series (ie no pre-trained feature extraction).", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Could the authors clarify what statistical test was performed to compare models\n- Could the authors provide more details about the fMRI preprocessing, construction of connectome time series that are input to the feature extractor, and justify the choice of AAL atlas and Pearson correlation.\n- Only a small subsample of the OASIS evaluation dataset is used; could the authors explain why? The small-data regime could still be simulated by using a small training set, while using the whole dataset for testing and thus producing more accurate evaluation scores.\n- Could the authors include information and a discussion about the computational cost of the proposed method and the baselines\n\n\ntypo in the introduction:\n\n- \"simply train a model\" -> \"simply training\"", "broader_impact_concerns": "none", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: I believe the latest submission has the changes made to account for reviewers' comments in blue. After the color is harmonized with the rest of the paper's text, the paper is ready.
Decision: Accept (poster)