AIMC Topic: Neuropsychological Tests

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A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

Journal of integrative neuroscience
The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem ...

Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease.

NeuroImage. Clinical
Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in...

The neural and computational bases of semantic cognition.

Nature reviews. Neuroscience
Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of r...

Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.

Human brain mapping
An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on t...

Believing androids - fMRI activation in the right temporo-parietal junction is modulated by ascribing intentions to non-human agents.

Social neuroscience
Attributing mind to interaction partners has been shown to increase the social relevance we ascribe to others' actions and to modulate the amount of attention dedicated to them. However, it remains unclear how the relationship between higher-order mi...

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

Schizophrenia research
Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG...

Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment.

Journal of attention disorders
Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of th...

Stress Detection Using Wearable Physiological and Sociometric Sensors.

International journal of neural systems
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physio...

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia.

NeuroImage. Clinical
Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of signifi...

Multivoxel Object Representations in Adult Human Visual Cortex Are Flexible: An Associative Learning Study.

Journal of cognitive neuroscience
Learning associations between co-occurring events enables us to extract structure from our environment. Medial-temporal lobe structures are critical for associative learning. However, the role of the ventral visual pathway (VVP) in associative learni...