Decoding diagnosis and lifetime consumption in alcohol dependence from grey‐matter pattern information

Abstract

Computer-based models applied to whole-brain grey-matter predicted diagnosis and lifetime consumption in AD with good accuracy. Computer-based classification may be particularly suited as a screening tool with high sensitivity.

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

“…Our results support our first hypothesis, showing that neural PIT signatures based on fMRI data gathered from the affective mixed-gambles task may successfully classify out-of-sample subjects into GD and HC, with a cross-validated mean AUC-ROC of 70.0% (p = 0.013). This performance on out-of sample data is similar to other studies using MRI data for classification in the field of addictive disorders (Guggenmos et al, 2018;Pariyadath, Stein, & Ross, 2014;Seo et al, 2018Seo et al, , 2015Whelan et al, 2014). To our knowledge, however, the present study is Alexander Genauck 32 the first one to use fMRI classification for investigating a behavioral addiction, namely GD, and the neural basis of increased PIT.…”

“…Using crossvalidation we assessed the generalizability of this classifier to new samples. Classifying GD and HC subjects using multivariate patterns aims to bring us closer to a clinically relevant characterization of the neural disturbances related to GD, especially when there are many relevant variables involved (Ahn, Ramesh, Moeller, & Vassileva, 2016;Cerasa et al, 2018;Guggenmos et al, 2018;Yarkoni & Westfall, 2017). To our knowledge, our study is the first one to use fMRI-based classification for investigating GD and its neural basis of increased PIT.…”

Artificial intelligence and machine learning in emergency medicine

“…Deep learning (DL) techniques have been shown to detect pneumonia on chest X‐ray accurately, achieve 3D segmentation of subdural haematomas on brain computed tomography (CT), and assess risk of cerebral aneurysm rupture and score CTs of patients with suspected acute ischaemic stroke as accurately as stroke specialists . DL applied to brain magnetic resonance imaging has been used to distinguish patients with a first episode psychosis from controls, and predict lifetime alcohol consumption . DL has also been applied to ultrasound (USG); demonstrating high accuracy in detecting abdominal free fluid on FAST (focused assessment with sonography for trauma) scans, classifying abdominal USG images and providing automated analysis of ejection fraction on echocardiogram .…”

“…[10][11][12][13] DL applied to brain magnetic resonance imaging has been used to distinguish patients with a first episode psychosis from controls, and predict lifetime alcohol consumption. 14,15 DL has also been applied to ultrasound (USG); demonstrating high accuracy in detecting abdominal free fluid on FAST (focused assessment with sonography for trauma) scans, classifying abdominal USG images and providing automated analysis of ejection fraction on echocardiogram. [16][17][18] DL has also enabled novel technologies, such as the use of a microwave based imaging helmet to accurately distinguish between ischaemic and haemorrhagic stroke in the prehospital environment.…”

Predicting future drinking among young adults: using ensemble machine-learning to combine MRI with psychometrics and behaviour

“…Second, this population differs from adolescent and older adult samples in that alcohol use has already been initiated, but the likelihood of alcohol-related brain changes remains low. More specifically, it has been found that factors that predict alcohol use in adolescence are not necessarily predictive in young adults (19)(20)(21) and this may be related to adolescents undergoing continuing profound neurobiological changes (22). Moreover, the importance of neurobiological versus psychometric and behavioural risk factors may be different before and after alcohol use initiation, and therefore in adolescents versus young adults.…”