Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks.

Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-class AD classification. Initial steps involve creating a patient-clinical graph network considering latent relationships among cognitive normal (CN), mild cognitive impairment (MCI), and AD patients, followed by training several GNN-based techniques for building prediction models.

A Statistical Characterization of Dynamic Brain Functional Connectivity.

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings.

Dissociating Cerebellar Regions Involved in Formulating and Articulating Words and Sentences.

We investigated which parts of the cerebellum are involved in formulating and articulating sentences using (i) a sentence production task that involved describing simple events in pictures; (ii) an auditory sentence repetition task involving the same sentence articulation but not sentence formulation; and (iii) an auditory sentence-to-picture matching task that involved the same pictorial events and no overt articulation. Activation for each of these tasks was compared to the equivalent word processing tasks: noun production, verb production, auditory noun repetition, and auditory noun-to-picture matching. We associate activation in bilateral cerebellum lobule VIIb with sequencing words into sentences because it increased for sentence production compared to all other conditions and was also activated by word production compared to word matching.

Advancing the analysis of water pipe failures: a probabilistic framework for identifying significant factors.

The failure of water pipes in Water Distribution Networks (WDNs) is associated with environmental, economic, and social consequences. It is essential to mitigate these failures by analyzing the historical data of WDNs. The extant literature regarding water pipe failure analysis is limited by the absence of a systematic selection of significant factors influencing water pipe failure and eliminating the bias associated with the frequency distribution of the historical data.

Symptomatology after damage to the angular gyrus through the lenses of modern lesion-symptom mapping.

Brain-behavior relationships are complex. For instance, one might know a brain region's function(s) but still be unable to accurately predict deficit type or severity after damage to that region. Here, I discuss the case of damage to the angular gyrus (AG) that can cause left-right confusion, finger agnosia, attention deficit, and lexical agraphia, as well as impairment in sentence processing, episodic memory, number processing, and gesture imitation.

Segmentation of stroke lesions using transformers-augmented MRI analysis.

Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans (e.g., T1-weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions.

Constraining current neuroanatomical models of reading: the view from Arabic.

There is a growing interest in imaging understudied orthographies to unravel their neuronal correlates and their implications for existing computational and neuroanatomical models. Here, we review current brain mapping literature about Arabic words. We first offer a succinct description of some unique linguistic features of Arabic that challenge current cognitive models of reading.

7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery.

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e.

Effect of Comorbidities Features in Machine Learning Models for Survival Analysis to Predict Prodromal Alzheimer's Disease.

Alzheimer's Disease (AD) is the most common form of dementia, specifically a progressive degenerative disorder affecting 47 million people worldwide and is only expected to grow in the elderly population. The detection of AD in its early stages is crucial to allow early intervention aiding in the prevention or slowing down of the disease. The effect of using comorbidity features in machine learning models to predict the time until a patient develops a prodrome was observed.

Cognitive decline assessment in speakers of understudied languages.

Unlabelled: Projected trends in population aging have forecasted a massive increase in the number of people with dementia, in particular in sub-Saharan Africa and the Middle East and North Africa (MENA) region. Cognitive decline is a significant marker for dementia, typically assessed with standardized neuropsychological tools that have been validated in some well-researched languages such as English. However, with the existing language diversity, current tools cannot cater to speakers of understudied languages, putting these populations at a disadvantage when it comes to access to early and accurate diagnosis of dementia.

Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals.

Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data.

An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a significant surge of interest in the development of supervised artificial intelligence (AI) methods for that purpose, including deep learning, with a performance comparable to trained experts. Such AI-based methods, however, require copious amounts of data.

Interpreting and validating complexity and causality in lesion-symptom prognoses.

This paper considers the steps needed to generate pragmatic and interpretable lesion-symptom mappings that can be used for clinically reliable prognoses. The novel contributions are 3-fold. We first define and inter-relate five neurobiological and five methodological constraints that need to be accounted for when interpreting lesion-symptom associations and generating synthetic lesion data.

The elusive metric of lesion load.

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns.

Multiple functions of the angular gyrus at high temporal resolution.

Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing.

A Data-Based Approach for Selecting Pre- and Intra-Operative Language Mapping Tasks.

Pre- and intra-operative language mapping in neurosurgery patients frequently involves an object naming task. The choice of the optimal object naming paradigm remains challenging due to lack of normative data and standardization in mapping practices. The aim of this study was to identify object naming paradigms that robustly and consistently activate classical language regions and could therefore be used to improve the sensitivity of language mapping in brain tumor and epilepsy patients.

An active human role is essential in big data-led decisions and data-intensive science.

Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data.

Lesions that do or do not impair digit span: a study of 816 stroke survivors.

Prior studies have reported inconsistency in the lesion sites associated with verbal short-term memory impairments. Here we asked: How many different lesion sites can account for selective impairments in verbal short-term memory that persist over time, and how consistently do these lesion sites impair verbal short-term memory? We assessed verbal short-term memory impairments using a forward digit span task from the Comprehensive Aphasia Test. First, we identified the incidence of digit span impairments in a sample of 816 stroke survivors (541 males/275 females; age at stroke onset 56 ± 13 years; time post-stroke 4.

Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture.

Reinforced concrete (RC) beams are basic elements used in the construction of various structures and infrastructural systems. When exposed to harsh environmental conditions, the integrity of RC beams could be compromised as a result of various deterioration mechanisms. One of the most common deterioration mechanisms is the formation of different types of corrosion in the steel reinforcements of the beams, which could impact the overall reliability of the beam.

Damage to Broca's area does not contribute to long-term speech production outcome after stroke.

Broca's area in the posterior half of the left inferior frontal gyrus has long been thought to be critical for speech production. The current view is that long-term speech production outcome in patients with Broca's area damage is best explained by the combination of damage to Broca's area and neighbouring regions including the underlying white matter, which was also damaged in Paul Broca's two historic cases. Here, we dissociate the effect of damage to Broca's area from the effect of damage to surrounding areas by studying long-term speech production outcome in 134 stroke survivors with relatively circumscribed left frontal lobe lesions that spared posterior speech production areas in lateral inferior parietal and superior temporal association cortices.