The application of tDCS in psychiatric disorders: a brain imaging view

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS) and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets, including 71 SZ patients and 71 HS. We created 16 3D-CAE models with different channels and convolutions to explore the effective range of hyperparameters for psychiatric brain imaging. The number of blocks containing two convolutional layers and one pooling layer was set, ranging from 1 block to 4 blocks. The number of channels in the extraction layer varied from 1, 4, 16, and 32 channels. The proposed 3D-CAEs were successfully reproduced into 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relation to clinical information. We explored the appropriate hyperparameter range of 3D-CAE, and it was suggested that a model with 3 blocks may be related to extracting features for predicting the dose of medication and symptom severity in schizophrenia.

Download Full-text

SPECT brain imaging in Psychiatric disorders: current clinical status

Brain SPECT Imaging in Psychiatry ◽

10.1515/9783110890655.35 ◽

2015 ◽

Cited By ~ 1

Author(s):

Ralph A. O'Connell

Keyword(s):

Psychiatric Disorders ◽

Brain Imaging ◽

Clinical Status

Download Full-text

Cross-species machine learning improves diagnostic classification of human psychiatric disorders

10.1101/812693 ◽

2019 ◽

Author(s):

Yafeng Zhan ◽

Jianze Wei ◽

Jian Liang ◽

Xiu Xu ◽

Ran He ◽

...

Keyword(s):

Machine Learning ◽

Psychiatric Disorders ◽

Brain Imaging ◽

Data Exchange ◽

Autism Spectrum ◽

Diagnostic Classification ◽

Imaging Data ◽

Primate Model ◽

Compulsive Disorder ◽

Brain Imaging Data

AbstractPsychiatric disorders often exhibit shared (co-morbid) symptoms, raising controversies over accurate diagnosis and the overlap of their neural underpinnings. Because the complexity of data generated by clinical studies poses a formidable challenge, we have pursued a reductionist framework using brain imaging data of a transgenic primate model of autism spectrum disorder (ASD). Here we report an interpretable cross-species machine learning approach which extracts transgene-related core regions in the monkey brain to construct the classifier for diagnostic classification in humans. The cross-species classifier based on core regions, mainly distributed in frontal and temporal cortex, identified from the transgenic primate model, achieved an accuracy of 82.14% in one clinical ASD cohort obtained from Autism Brain Imaging Data Exchange (ABIDE-I), significantly higher than the human-based classifier (61.31%, p < 0.001), which was validated in another independent ASD cohort obtained from ABIDE-II. Such monkey-based classifier generalized to achieve a better classification in obsessive-compulsive disorder (OCD) cohorts, and enabled parsing of differential connections to right ventrolateral prefrontal cortex being attributable to distinct traits in patients with ASD and OCD. These findings underscore the importance of investigating biologically homogeneous samples, particularly in the absence of real-world data adequate for deconstructing heterogeneity inherited in the clinical cohorts.One Sentence SummaryFeatures learned from transgenic monkeys enable improved diagnosis of autism-related disorders and dissection of their underlying circuits.

Download Full-text

Brain imaging and psychiatric disorders

Current Opinion in Psychiatry ◽

10.1097/00001504-199002000-00019 ◽

1990 ◽

Vol 3 (1) ◽

pp. 79-82 ◽

Cited By ~ 2

Author(s):

K R R Krishnan

Keyword(s):

Psychiatric Disorders ◽

Brain Imaging

Download Full-text

Are neurological and psychiatric disorders different?

The British Journal of Psychiatry ◽

10.1192/bjp.bp.114.158550 ◽

2015 ◽

Vol 207 (5) ◽

pp. 373-374 ◽

Cited By ~ 4

Author(s):

Anthony S. David ◽

Timothy Nicholson

Keyword(s):

Psychiatric Disorders ◽

Brain Imaging ◽

Imaging Data ◽

Brain Imaging Data ◽

Structural Brain ◽

Structural Brain Imaging

SummaryThere have been recent calls to abandon the distinction between neurological and psychiatric disorders on philosophical and moral grounds. Crossley and colleagues, in this issue, meta-analyse published structural brain imaging data and prove that they are different after all – or do they?

Download Full-text

Brain imaging could further understanding of psychiatric disorders

Physics Today ◽

10.1063/pt.5.027520 ◽

2013 ◽

Keyword(s):

Psychiatric Disorders ◽

Brain Imaging

Download Full-text

Application of Noninvasive Vagal Nerve Stimulation to Stress-Related Psychiatric Disorders

Journal of Personalized Medicine ◽

10.3390/jpm10030119 ◽

2020 ◽

Vol 10 (3) ◽

pp. 119 ◽

Cited By ~ 2

Author(s):

James Douglas Bremner ◽

Nil Z. Gurel ◽

Matthew T. Wittbrodt ◽

Mobashir H. Shandhi ◽

Mark H. Rapaport ◽

...

Keyword(s):

Vagus Nerve ◽

Psychiatric Disorders ◽

Brain Imaging ◽

Nerve Stimulation ◽

Vagal Nerve ◽

Vagal Nerve Stimulation ◽

Favorable Effect ◽

Blood Biomarkers ◽

Wearable Sensing ◽

Widespread Implementation

Background: Vagal Nerve Stimulation (VNS) has been shown to be efficacious for the treatment of depression, but to date, VNS devices have required surgical implantation, which has limited widespread implementation. Methods: New noninvasive VNS (nVNS) devices have been developed which allow external stimulation of the vagus nerve, and their effects on physiology in patients with stress-related psychiatric disorders can be measured with brain imaging, blood biomarkers, and wearable sensing devices. Advantages in terms of cost and convenience may lead to more widespread implementation in psychiatry, as well as facilitate research of the physiology of the vagus nerve in humans. nVNS has effects on autonomic tone, cardiovascular function, inflammatory responses, and central brain areas involved in modulation of emotion, all of which make it particularly applicable to patients with stress-related psychiatric disorders, including posttraumatic stress disorder (PTSD) and depression, since dysregulation of these circuits and systems underlies the symptomatology of these disorders. Results: This paper reviewed the physiology of the vagus nerve and its relevance to modulating the stress response in the context of application of nVNS to stress-related psychiatric disorders. Conclusions: nVNS has a favorable effect on stress physiology that is measurable using brain imaging, blood biomarkers of inflammation, and wearable sensing devices, and shows promise in the prevention and treatment of stress-related psychiatric disorders.

Download Full-text