International Journal of Neural Systems

Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data. Here, we proposed a robust FL method to efficiently tackle data and model heterogeneity, where we train our model using knowledge distillation and a novel weighted client confidence score on hematological cytomorphology data in clinical settings. In the knowledge distillation, each participant learns from other participants by a weighted confidence score so that knowledge from clean models is distributed other than the noisy clients possessing noisy data. Moreover, we use symmetric loss to reduce the negative impact of data heterogeneity and label diversity by reducing overfitting the model to noisy labels. In comparison to the current approaches, our proposed method performs the best, and this is the first demonstration of addressing both data and model heterogeneity in end-to-end FL that lays the foundation for robust FL in laboratories and clinical applications.

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain’s structural–functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural–functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural–functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.

Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node–node and edge–edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.

Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named $ϵ$-net. We provide a proof of convergence for the $ϵ$-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.

Constructing computational decoding models to account for the cortical representation of semantic information plays a crucial role in understanding visual perception. The human visual system processes interactive relationships among different objects when perceiving the semantic contents of natural visions. However, the existing semantic decoding models commonly regard categories as completely separate and independent visually and semantically and rarely consider the relationships from prior information. In this work, a novel semantic graph learning model was proposed to decode multiple semantic categories of perceived natural images from brain activity. The proposed model was validated on the functional magnetic resonance imaging data collected from five normal subjects while viewing 2750 natural images comprising 52 semantic categories. The results showed that the Graph Neural Network-based decoding model achieved higher accuracies than other deep neural network models. Moreover, the co-occurrence probability among semantic categories showed a significant correlation with the decoding accuracy. Additionally, the results suggested that semantic content organized in a hierarchical way with higher visual areas was more closely related to the internal visual experience. Together, this study provides a superior computational framework for multi-semantic decoding that supports the visual integration mechanism of semantic processing.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.