AIMC Topic: Functional Neuroimaging

Clear Filters Showing 1 to 10 of 70 articles

An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test.

Asian journal of psychiatry
BACKGROUND: Individuals with anxious-depressed (AD) symptoms have more severe mood disorders and cognitive impairment than those with non-anxious depression (NAD) symptoms. Therefore, it is important to clarify the underlying neurophysiology of these...

Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.

Translational psychiatry
Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functio...

Effects of Individual Research Practices on fNIRS Signal Quality and Latent Characteristics.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of ...

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

Contemporary clinical trials
BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PS...

Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces.

Brain research bulletin
Conventional cognitive assessment is widely used in clinical and research settings, in educational institutions, and in the corporate world for personnel selection. Such approaches involve having a client, a patient, or a research participant complet...

Meta-analysis of the functional neuroimaging literature with probabilistic logic programming.

Scientific reports
Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and...

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis.

NeuroImage. Clinical
Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically smal...

Contrastive machine learning reveals the structure of neuroanatomical variation within autism.

Science (New York, N.Y.)
Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual differences in neuroanatomy could inform diagnosis and personalized interventions. The challenge is that these differences are entangled with variation because ...

Brain Decoding Using fMRI Images for Multiple Subjects through Deep Learning.

Computational and mathematical methods in medicine
Substantial information related to human cerebral conditions can be decoded through various noninvasive evaluating techniques like fMRI. Exploration of the neuronal activity of the human brain can divulge the thoughts of a person like what the subjec...