Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy (r = .78) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure.

In clinical neuroscience, the segmentation of the main white matter bundles is propaedeutic for many tasks such as pre-operative neurosurgical planning and monitoring of neuro-related diseases. Automating bundle segmentation with data-driven approaches and deep learning models has shown promising accuracy in the context of healthy individuals. The lack of large clinical datasets is preventing the translation of these results to patients. Inference on patients data with models trained on healthy population is not effective because of domain shift. This study aims to carry out an empirical analysis to investigate how transfer learning might be beneficial to overcome these limitations. For our analysis, we consider a public dataset with hundreds of individuals and a clinical dataset of glioma patients. We focus our preliminary investigation on the corticospinal tract. The results show that transfer learning might be effective in partially overcoming the domain shift.

The development of therapeutics builds on testing their efficiency in vitro. To optimize gene therapies, for example, fluorescent reporters expressed by treated cells are typically utilized as readouts. Traditionally, their global fluorescence signal has been used as an estimate of transduction efficiency. However, analysis in individual cells within a living 3D tissue remains a challenge. Readout on a single-cell level can be realized via fluo-rescence-based flow cytometry at the cost of tissue dissociation and loss of spatial information. Complementary, spatial information is accessible via immunofluorescence of fixed samples. Both approaches impede time-dependent studies on the delivery of the vector to the cells. Here, quantitative 3D characterization of viral transduction efficiencies in living retinal organoids is introduced. The approach combines quantified gene delivery efficiency in space and time, leveraging human retinal organ-oids, engineered adeno-associated virus (AAV) vectors, confocal live imaging, and deep learning-based image segmentation. The integration of these tools in an organoid imaging and analysis pipeline allows quantitative testing of future treatments and other gene delivery methods. It has the potential to guide the development of therapies in biomedical applications.

In neuroimaging research, the utilization of multi-centric analyses is crucial for obtaining sufficient sample sizes and representative clinical populations. Data harmonization techniques are typically part of the pipeline in multi-centric studies to address systematic biases and ensure the comparability of the data. However, most multi-centric studies require centralized data, which may result in exposing individual patient information. This poses a significant challenge in data governance, leading to the implementation of regulations such as the GDPR and the CCPA, which attempt to address these concerns but also hinder data access for researchers. Federated learning offers a privacy-preserving alternative approach in machine learning, enabling models to be collaboratively trained on decentralized data without the need for data centralization or sharing. In this paper, we present Fed-ComBat, a federated framework for batch effect harmonization on decentralized data. Fed-ComBat extends existing centralized linear methods, such as ComBat and distributed as d-ComBat, and nonlinear approaches like ComBat-GAM in accounting for potentially nonlinear and multivariate covariate effects. By doing so, Fed-ComBat enables the preservation of nonlinear covariate effects without requiring centralization of data and without prior knowledge of which variables should be considered nonlinear or their interactions, differentiating it from ComBat-GAM. We assessed Fed-ComBat and existing approaches on simulated data and multiple cohorts comprising healthy controls (CN) and subjects with various disorders such as Parkinson's disease (PD), Alzheimer's disease (AD), and autism spectrum disorder (ASD). The results of our study show that Fed-ComBat performs better than centralized ComBat when dealing with nonlinear effects and is on par with centralized methods like ComBat-GAM. Through experiments using synthetic data, Fed-ComBat demonstrates a superior ability to reconstruct the target unbiased function, achieving a 35% improvement (RMSE=0.5952) compared to d-ComBat (RMSE=0.9162) and a 12% improvement compared to our proposal to federate ComBat-GAM, d-ComBat-GAM (RMSE=0.6751). Additionally, Fed-ComBat achieves comparable results to centralized methods like ComBat-GAM for MRI-derived phenotypes without requiring prior knowledge of potential nonlinearities.

Each year, thousands of brain MRI scans are collected to study structural development in children and adolescents. However, the amygdala, a particularly small and complex structure, remains difficult to segment reliably, especially in developing populations where its volume is even smaller. To address this challenge, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model tailored for human amygdala segmentation. It was trained and validated using 854 manually labeled scans from pediatric datasets, with independent samples used to ensure performance generalizability. The model integrates multiscale image features, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Validation across multiple imaging centers and age groups shows that AmygdalaGo-BOLT closely matches expert manual labels, improves processing efficiency, and outperforms existing tools in accuracy. This enables robust and scalable analysis of amygdala morphology in developmental neuroimaging studies where manual tracing is impractical. To support open and reproducible science, we publicly release both the labeled datasets and the full source code.

Alzheimer's disease (AD) progressively alters brain structure and function, yet the associated changes in large-scale brain network dynamics remain poorly understood. We applied the intrinsic ignition framework to resting-state functional MRI (rs-fMRI) data from AD patients, individuals with mild cognitive impairment (MCI), and cognitively healthy controls (HC) to elucidate how AD shapes intrinsic brain activity. We assessed node-metastability at the whole-brain level and in 7 canonical resting-state networks (RSNs). Our results revealed a progressive decline in dynamical complexity across the disease continuum. HC exhibited the highest node-metastability, whereas it was substantially reduced in MCI and AD patients. The cortical hierarchy of information processing was also disrupted, indicating that rich-club hubs may be selectively affected in AD progression. Furthermore, we used linear mixed-effects models to evaluate the influence of Amyloid-{beta} (A{beta}) and tau pathology on brain dynamics at both regional and whole-brain levels. We found significant associations between both protein burdens and alterations in node metastability. Lastly, a machine learning classifier trained on brain dynamics, A{beta}, and tau burden features achieved high accuracy in discriminating between disease stages. Together, our findings highlight the progressive disruption of intrinsic ignition across whole-brain and RSNs in AD and support the use of node-metastability in conjunction with proteinopathy as a novel framework for tracking disease progression.