AIMC Topic: Brain

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Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms f...

Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of h...

Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity.

Journal of neural engineering
OBJECTIVE: Somatic symptom disorder (SSD) is a reflection of medically unexplained physical symptoms that lead to distress and impairment in social and occupational functioning. SSD is phenomenologically diagnosed and its neurobiology remains unsolve...

From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior.

Neural computation
The study of neuronal interactions is at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, and the Brainome) that attempt to obtain a detailed map of the entire brain. Under...

Polydopamine Nanoparticles for Deep Brain Ablation via Near-Infrared Irradiation.

ACS biomaterials science & engineering
Local resection or ablation remains an important approach to treat drug-resistant central neurological disease. Conventional surgical approaches are designed to resect the diseased tissues. The emergence of photothermal therapy (PTT) offers a minimal...

Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning.

Neural plasticity
According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphologi...

Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.

Biological psychiatry
BACKGROUND: In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g...

Decoding and mapping task states of the human brain via deep learning.

Human brain mapping
Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requir...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

NeuroImage
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a cons...

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.

NeuroImage. Clinical
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-...