However,itisstill not understood howthe task itself contributes to this difference.

Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning models have shown high accuracy in AD diagnosis, their lack of interpretability limits clinical trust and adoption. This paper introduces a novel pre-model approach leveraging Jacobian Maps (JMs) within a multi-modal framework to enhance explainability and trustworthiness in AD detection. By capturing localized brain volume changes, JMs establish meaningful correlations between model predictions and well-known neuroanatomical biomarkers of AD. We validate JMs through experiments comparing a 3D CNN trained on JMs versus on traditional preprocessed data, which demonstrates superior accuracy. We also employ 3D Grad-CAM analysis to provide both visual and quantitative insights, further showcasing improved interpretability and diagnostic reliability.

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alzheimer's disease (AD) onset and progression remains unclear. This study aimed to explore whether the presence and location of lobar microbleeds are associated with amyloid-$\beta$ (A$\beta$)-PET, tau tangle formation (tau-PET), and longitudinal cognitive decline. We analyzed 1,573 ADNI participants with MR imaging data and information on the number and location of microbleeds. Associations between lobar microbleeds and pathology, cerebrospinal fluid (CSF), genetics, and cognition were examined, focusing on regional microbleeds and domain-specific cognitive decline using ordinary least-squares regression while adjusting for covariates. Cognitive decline was assessed with ADAS-Cog11 and its domain-specific sub-scores. Participants underwent neuropsychological testing at least twice, with a minimum two-year interval between assessments. Among the 1,573 participants (692 women, mean age 71.23 years), 373 participants had microbleeds. The presence of microbleeds was linked to cognitive decline, particularly in the semantic, language, and praxis domains for those with temporal lobe microbleeds. Microbleeds in the overall cortex were associated with language decline. Pathologically, temporal lobe microbleeds were associated with increased tau in the overall cortex, while cortical microbleeds were linked to elevated A$\beta$ in the temporal, parietal, and frontal regions. In this mixed population, microbleeds were connected to longitudinal cognitive decline, especially in semantic and language domains, and were associated with higher baseline A$\beta$ and tau pathology. These findings suggest that lobar microbleeds should be included in AD diagnostic and prognostic evaluations.

The process of meaning composition, wherein smaller units like morphemes or words combine to form the meaning of phrases and sentences, is essential for human sentence comprehension. Despite extensive neurolinguistic research into the brain regions involved in meaning composition, a computational metric to quantify the extent of composition is still lacking. Drawing on the key-value memory interpretation of transformer feed-forward network blocks, we introduce the Composition Score, a novel model-based metric designed to quantify the degree of meaning composition during sentence comprehension. Experimental findings show that this metric correlates with brain clusters associated with word frequency, structural processing, and general sensitivity to words, suggesting the multifaceted nature of meaning composition during human sentence comprehension.

Trust is the foundation of every area of life. Without it, it is difficult to build lasting relationships. Unfortunately, in recent years, trust has been severely damaged by the spread of fake news and disinformation, which has become a serious social problem. In addition to trust, the factor influencing interpersonal relationships is perceived attractiveness, which is currently created to a large extent by digital media. Understanding the principles of judging others can be helpful in fighting prejudice and rebuilding trust in society. One way to learn about people's choices is to record their brain activity as they make choices. The article presents an experiment in which the faces of different people were presented, and the participants' task was to assess how much they can trust a given person and how attractive they are. During the study, the EEG signal was recorded, which was used to build models of logistic regression classifiers. In addition, the most active areas of the brain that participate in the assessment of trust and attractiveness of the face were indicated.

Bruce Willis has been diagnosed with frontotemporal dementia (FTD), his family says, nearly a year after the Die Hard franchise star retired from acting because aphasia had hampered his cognitive abilities. "Since we announced Bruce's diagnosis of aphasia in spring 2022, Bruce's condition has progressed and we now have a more specific diagnosis: frontotemporal dementia," his family said in a statement posted on The Association for Frontotemporal Degeneration website on Thursday. "Unfortunately, challenges with communication are just one symptom of the disease Bruce faces. While this is painful, it is a relief to finally have a clear diagnosis." FTD is an umbrella term for disorders affecting the areas of the brain that deal with personality, behaviour, language, and/or movement.

In the domain of face recognition, there exists a puzzling timing discrepancy between results from macaque neurophysiology on the one hand and human electrophysiology on the other. Single unit recordings in macaques have demonstrated face identity specific responses in extra-striate visual cortex within 100 milliseconds of stimulus onset. In EEG and MEG experiments with humans, however, a consistent distinction between neural activity corresponding to unfamiliar and familiar faces has been reported to emerge around 250 ms. This points to the possibility that there may be a hitherto undiscovered early correlate of face familiarity perception in human electrophysiological traces. We report here a successful search for such a correlate in dense MEG recordings using pattern classification techniques. Our analyses reveal markers of face familiarity as early as 85 ms after stimulus onset. Low-level attributes of the images, such as luminance and color distributions, are unable to account for this early emerging response difference. These results help reconcile human and macaque data, and provide clues regarding neural mechanisms underlying familiar face perception.

The question must have fascinated everyone? A very simple question -- how and why does Emotions come from? The question can be looked from various aspect s-- there is a perspective of psychology, we can look through the prism of cognitive science and can also try to find our answer through our "limited" knowledge of our Brain. Human brain is a very complex organ. It controls and coordinates everything from the movement of your lips to your heart rate.

The outer layer of the cerebral hemisphere is termed the cerebral cortex. This is inter-connected via pathways that run sub-cortically. It is these connections as well as the connections from the cerebral cortex to the brainstem, spinal cord and nuclei deep within the cerebral hemisphere that form the white matter of the cerebral hemisphere. The deep nuclei include structures such as the basal ganglia and the thalamus. The main difference between cerebrum and cerebral cortex is that cerebrum is the largest part of the brain whereas cerebral cortex is the outer layer of the cerebrum.