scholarly journals Transdiagnostic connectome-based prediction of craving

2021 ◽  
Author(s):  
Kathleen A Garrison ◽  
Rajita Sinha ◽  
Marc N Potenza ◽  
Siyuan Gao ◽  
Qinghao Liang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Guided Imagery ◽  
Posterior Cingulate Cortex ◽  
Learning Approach ◽  
Imagery Condition ◽  
Addictive Disorders ◽  
Large N ◽  
Machine Learning Approach ◽  
Before And After ◽  
Directing Attention

Craving is a central construct in the study of motivation and human behavior and is also a clinical symptom of substance and non-substance-related addictive disorders. Thus, craving represents a target for transdiagnostic modeling. We applied connectome-based predictive modeling (CPM) to functional connectivity data in a large (N=274) transdiagnostic sample of individuals with and without substance-use-related conditions, to predict self-reported craving. CPM is a machine-learning approach used to identify neural signatures in functional connectivity data related to a specific phenotype. Functional connectomes were derived from three guided imagery conditions of personalized appetitive, stress, and neutral-relaxing experiences. Craving was rated before and after each imagery condition. CPM successfully predicted craving, thereby identifying a transdiagnostic craving network comprised primarily of the posterior cingulate cortex, hippocampus, visual cortex, and primary sensory areas. Findings suggest that craving may be associated with difficulties directing attention away from internal self-related processing, represented in the default mode network.

scholarly journals An Investigation of Battery Electric Vehicle Driving and Charging Behaviors Using Vehicle Usage Data Collected in Shanghai, China

2018 ◽  
Vol 2672 (24) ◽  
pp. 20-30 ◽  
Author(s):  
Jie Yang ◽  
Jing Dong ◽  
Qi Zhang ◽  
Zhiyuan Liu ◽  
Wei Wang
Keyword(s):  
Electric Vehicle ◽  
Time Of Day ◽  
State Of Charge ◽  
Learning Approach ◽  
Battery Electric Vehicle ◽  
Summary Statistics ◽  
Start Time ◽  
Machine Learning Approach ◽  
Before And After ◽  
Battery State Of Charge

This paper investigates the driving and charging behaviors of battery electric vehicle (BEV) drivers observed in Shanghai, China. The summary statistics are compared with the observations from the U.S. EV Project. A machine-learning approach, namely self-organizing feature map (SOM), is adopted as a classifier to analyze BEV drivers’ habitual behaviors. The inter-driver heterogeneities are examined in terms of: the distributions of distance traveled per day, the start time of charging, the number of charges per day, distance traveled between consecutive charges, battery state of charge (SOC) before and after charging, and time-of-day electricity demand. It is found that ( a) BEV drivers demonstrate conservative charging behaviors, leading to short distances between consecutive charging events; ( b) a significant number of BEV drivers in Shanghai charge during daytime; ( c) the distributions depicting the driving and charging patterns vary greatly due to the diversity in travel activities among different drivers.

scholarly journals Somatosensory network functional connectivity differentiates clinical pain phenotypes in diabetic neuropathy

Diabetologia ◽  
2021 ◽  
Author(s):  
Kevin Teh ◽  
Iain D. Wilkinson ◽  
Francesca Heiberg-Gibbons ◽  
Mohammed Awadh ◽  
Alan Kelsall ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neuropathic Pain ◽  
Functional Connectivity ◽  
Somatosensory Cortex ◽  
Resting State ◽  
Insular Cortex ◽  
Brain Regions ◽  
Learning Approach ◽  
Clinical Pain ◽  
Machine Learning Approach

Abstract Aims/hypothesis The aim of this work was to investigate whether different clinical pain phenotypes of diabetic polyneuropathy (DPN) are distinguished by functional connectivity at rest. Methods This was an observational, cohort study of 43 individuals with painful DPN, divided into irritable (IR, n = 10) and non-irritable (NIR, n = 33) nociceptor phenotypes using the German Research Network of Neuropathic Pain quantitative sensory testing protocol. In-situ brain MRI included 3D T1-weighted anatomical and 6 min resting-state functional MRI scans. Subgroup differences in resting-state functional connectivity in brain regions involved with somatic (thalamus, primary somatosensory cortex, motor cortex) and non-somatic (insular and anterior cingulate cortices) pain processing were examined. Multidimensional reduction of MRI datasets was performed using a machine-learning approach to classify individuals into each clinical pain phenotype. Results Individuals with the IR nociceptor phenotype had significantly greater thalamic–insular cortex (p false discovery rate [FDR] = 0.03) and reduced thalamus–somatosensory cortex functional connectivity (p-FDR = 0.03). We observed a double dissociation such that self-reported neuropathic pain score was more associated with greater thalamus–insular cortex functional connectivity (r = 0.41; p = 0.01) whereas more severe nerve function deficits were more related to lower thalamus–somatosensory cortex functional connectivity (r = −0.35; p = 0.03). Machine-learning group classification performance to identify individuals with the NIR nociceptor phenotype achieved an accuracy of 0.92 (95% CI 0.08) and sensitivity of 90%. Conclusions/interpretation This study demonstrates differences in functional connectivity in nociceptive processing brain regions between IR and NIR phenotypes in painful DPN. We also establish proof of concept for the utility of multimodal MRI as a biomarker for painful DPN by using a machine-learning approach to classify individuals into sensory phenotypes. Graphical abstract

scholarly journals The Exercising Brain: Changes in Functional Connectivity Induced by an Integrated Multimodal Cognitive and Whole-Body Coordination Training

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Traute Demirakca ◽  
Vita Cardinale ◽  
Sven Dehn ◽  
Matthias Ruf ◽  
Gabriele Ende
Keyword(s):  
Functional Connectivity ◽  
Brain Plasticity ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Whole Body ◽  
Before And After ◽  
Coordination Training ◽  
Brain Changes ◽  
The Impact ◽  
The Brain

This study investigated the impact of “life kinetik” training on brain plasticity in terms of an increased functional connectivity during resting-state functional magnetic resonance imaging (rs-fMRI). The training is an integrated multimodal training that combines motor and cognitive aspects and challenges the brain by introducing new and unfamiliar coordinative tasks. Twenty-one subjects completed at least 11 one-hour-per-week “life kinetik” training sessions in 13 weeks as well as before and after rs-fMRI scans. Additionally, 11 control subjects with 2 rs-fMRI scans were included. The CONN toolbox was used to conduct several seed-to-voxel analyses. We searched for functional connectivity increases between brain regions expected to be involved in the exercises. Connections to brain regions representing parts of the default mode network, such as medial frontal cortex and posterior cingulate cortex, did not change. Significant connectivity alterations occurred between the visual cortex and parts of the superior parietal area (BA7). Premotor area and cingulate gyrus were also affected. We can conclude that the constant challenge of unfamiliar combinations of coordination tasks, combined with visual perception and working memory demands, seems to induce brain plasticity expressed in enhanced connectivity strength of brain regions due to coactivation.

scholarly journals Functional connectivity of music-induced analgesia in fibromyalgia

2017 ◽  
Author(s):  
Victor Pando-Naude ◽  
Fernando A. Barrios ◽  
Sarael Alcauter ◽  
Erick H. Pasaye ◽  
Lene Vase ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Positive Emotion ◽  
Neural Correlates ◽  
Posterior Cingulate Cortex ◽  
Auditory Attention ◽  
Angular Gyrus ◽  
Music Intervention ◽  
Resting State Functional Connectivity ◽  
Before And After ◽  
Pain Reports

ABSTRACTListening to self-chosen, pleasant and relaxing music reduces pain in fibromyalgia (FM), a chronic central pain condition. However, the neural correlates of this effect are fairly unknown and could be regarded as a more direct measure of analgesia. In our study, we wished to investigate the neural correlates of music-induced analgesia (MIA) in fibromyalgia patients. To do this, we studied 20 FM patients and 20 matched healthy controls (HC) acquiring rs-fMRI with a 3T MRI scanner, and pain data before and after two 5-min auditory conditions: music and noise. We performed resting state functional connectivity (rs-FC) seed-based correlation analyses (SCA) using pain and analgesia-related ROIs to determine the effects before and after the music intervention in FM and HC, and its correlation with pain reports. We found significant differences in baseline rs-FC between FM and HC. Both groups showed changes in rs-FC in several ROIs after the music condition between different areas, that were left lateralized in FM and right lateralized in HC. FM patients reported MIA that was significantly correlated with rs-FC decrease between the angular gyrus, posterior cingulate cortex and precuneus, and rs-FC increase between amygdala and middle frontal gyrus. These areas are related to autobiographical and limbic processes, and auditory attention, suggesting MIA may arise as a consequence of top-down modulation, probably originated by distraction, relaxation, positive emotion, or a combination of these mechanisms.

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