Aggregating intracranial recordings across subjects is challenging since electrode count, placement, and covered regions vary widely. Spatial normalization methods like MNI coordinates offer a shared anatomical reference, but often fail to capture true functional similarity, particularly when localization is imprecise; even at matched anatomical coordinates, the targeted brain region and underlying neural dynamics can differ substantially between individuals. We propose a scalable representation-learning framework that (i) learns a subject-agnostic functional identity for each electrode from multi-region local field potentials using a Siamese encoder with contrastive objectives, inducing an embedding geometry that is locality-sensitive to region-specific neural signatures, and (ii) tokenizes these embeddings for a transformer that models inter-regional relationships with a variable number of channels. We evaluate this framework on a 20-subject dataset spanning basal ganglia-thalamic regions collected during flexible rest/movement recording sessions with heterogeneous electrode layouts. The learned functional space supports accurate within-subject discrimination and forms clear, region-consistent clusters; it transfers zero-shot to unseen channels. The transformer, operating on functional tokens without subject-specific heads or supervision, captures cross-region dependencies and enables reconstruction of masked channels, providing a subject-agnostic backbone for downstream decoding. Together, these results indicate a path toward large-scale, cross-subject aggregation and pretrain-ing for intracranial neural data where strict task structure and uniform sensor placement are unavailable. Building models that generalize across subjects in neuroscience requires representations that remain stable despite variability in data acquisition. Intracranial neural recordings lack this stability: electrode locations, counts, sampling, and coverage differ across individuals, reflecting clinical needs rather than standardized layouts. Without a shared representational system, cross-subject aggregation is unreliable, limiting scalable modeling and clinical translation. Such recordings are uniquely valuable for studying inter-regional communication, yet their heterogeneity makes them especially challenging to align. In practice, two obstacles dominate: Anatomical variability and inconsistent electrode coverage.

Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.937 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that responsible for processing self-referential information and environmental context information are impaired.

Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by different cognitive states. This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality -- such as whole-brain images. Unlike the previous methods, DRSL is not limited by a linear transformation or a restricted fixed nonlinear kernel function -- such as Gaussian kernel. DRSL utilizes a multi-layer neural network for mapping neural responses to linear space, where this network can implement a customized nonlinear transformation for each subject separately. Furthermore, utilizing a gradient-based optimization in DRSL can significantly reduce runtime of analysis on large datasets because it uses a batch of samples in each iteration rather than all neural responses to find an optimal solution. Empirical studies on multi-subject fMRI datasets with various tasks -- including visual stimuli, decision making, flavor, and working memory -- confirm that the proposed method achieves superior performance to other state-of-the-art RSA algorithms.

Suicide is the world's second leading cause of death among youth. According to World Health Organisation, almost 8,00,000 people commit suicides every year; and the number is growing alarmingly fast. The challenging task which lies ahead to mitigate suicides, is the assessment related to suicidal risks or tendencies. Mental health professionals fare poorer in their predictions when it comes to assessing patients' future suicidal tendencies. This is mostly due to the fact that patient hide their suicidal ideations at the time of counselling.

Mention strong words such as "death" or "praise" to someone who has suicidal thoughts and chances are the neurons in their brains activate in a totally different pattern than those of a non-suicidal person. That's what researchers at University of Pittsburgh and Carnegie Mellon University discovered, and trained algorithms to distinguish, using data from fMRI brain scans. The scientists published the findings of their small-scale study Monday in the journal Nature Human Behaviour. They hope to study a larger group of people and use the data to develop simple tests that doctors can use to more readily identify people at risk of suicide. Suicide is the second-leading cause of death among young adults, according to the U.S. Centers for Disease Control and Prevention.

When a person's distress, depression or discouragement appears to have taken a sharp turn for the worse, it's time to ask him or her a weighty question: Are you thinking of harming yourself? If only the answer were a better guide. One study has found that nearly 80% of patients who took their own lives denied they were contemplating suicide in their last contact with a mental healthcare professional. Friends and family suffer the guilt and anguish of not having divined a loved one's intentions, but mental health professionals rarely fare much better at doing so. But what if the brain's response to a series of questions -- never the question, but a more indirect probe of a person's feelings -- yielded a more accurate signal?

Visual recognition of objects may rely on different features depending on the category to which they belong. Recognizing natural objects, such as fruits and plants, weighs more on their perceptual attributes, whereas recognizing man-made objects, such as tools or vehicles, weighs more upon the functions and actions they enable. Edible objects are perceptually rich but also prepared for specific functions, therefore it is unclear how perceptual and functional attributes affect their recognition. Two event-related potentials experiments investigated: (i) whether food categorization in the brain is differentially modulated by sensory and functional attributes, depending on whether the food is natural or transformed; (ii) whether these processes are modulated by participants' body mass index. In experiment 1, healthy normal-weight participants were presented with a sentence (prime) and a photograph of a food.