Brain Imaging and Behavior

Correction to: Internet gaming disorder: deficits in functional and structural connectivity in the ventral tegmental area-Accumbens pathway The original version of this article contained mistakes, and the authors would like to correct them. The correct details are given below:

Correction to: Relevance of neuroimaging for neurocognitive and behavioral outcome after pediatric traumatic brain injury JC Goslings is the correct name of the sixth author of this article.

Brain response to food brands correlates with increased intake from branded meals in children: an fMRI study Abstract Food branding is ubiquitous, however, not all children are equally susceptible to its effects. The objectives of this study were to 1) determine whether food brands evoke differential response than non-food brands in brain areas related to motivation and inhibitory control using blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and 2) determine the association between brain response and energy intake at test-meals presented with or without brands. Twenty-eight 7–10 year-old children completed four visits as part of a within-subjects design where they consumed three multi-item test-meals presented with familiar food brands, novel food brand, and no brand. On the fourth visit an fMRI was performed where children passively viewed food brands, non-food brands and control images. A whole-brain analysis was conducted to compare BOLD response between conditions. Pearson’s correlations were calculated to determine the association between brain response and meal intake. Relative to non-food brands, food brand images were associated with increased activity in the right lingual gyrus. Relative to control, food and non-food brand images were associated with greater response in bilateral fusiform gyri and decreased response in the cuneus, precuneus, lingual gyrus, and supramarginal gyrus. Less activation in the bilateral fusiform gyrus to both food and non-food brands was associated with greater energy intake of the branded vs unbranded meal. These findings may help explain differences in the susceptibility to the intake-promoting effects of food advertising in children.

Encoding the local connectivity patterns of fMRI for cognitive task and state classification Abstract In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Altered reward-related neural responses in non-manifesting carriers of the Parkinson disease related LRRK2 mutation Abstract Disturbances in reward processing occur in Parkinson’s disease (PD) however it is unclear whether these are solely drug-related. We applied an event-related fMRI gambling task to a group of non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene, in order to assess the reward network in an “at risk” population for future development of PD. Sixty-eight non-manifesting participants, 32 of which were non-manifesting non-carriers (NMNC), performed a gambling task which included defined intervals of anticipation and response to both reward and punishment in an fMRI setup. Behavior and cerebral activations were measured using both hypothesis driven and whole brain analysis. NMC demonstrated higher trait anxiety scores (p = 0.04) compared to NMNC. Lower activations were detected among NMC during risky anticipation in the left nucleus accumbens (NAcc) (p = 0.05) and during response to punishment in the right insula (p = 0.02), with higher activations among NMC during safe anticipation in the right insula (p = 0.02). Psycho-Physiological Interaction (PPI) analysis from the NAcc and insula revealed differential connectivity patterns. Whole brain analysis demonstrated divergent between-group activations in distributed cortical regions, bilateral caudate, left midbrain, when participants were required to press the response button upon making their next chosen move. Abnormal neural activity in both the reward and motor networks were detected in NMC indicating involvement of the ventral striatum regardless of medication use in “at risk” individuals for future development of PD.

Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data Abstract Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.

Long-term brain effects of N -back training: an fMRI study Abstract Neurobehavioral effects of cognitive training have become a popular research issue. Specifically, behavioral studies have demonstrated the long-term efficacy of cognitive training of working memory functions, but the neural basis for this training have been studied only at short-term. Using fMRI, we investigate the cerebral changes produced by brief single n-back training immediately and 5 weeks after finishing the training. We used the data from a sample of 52 participants who were assigned to either an experimental condition (training group) or a no-contact control condition. Both groups completed three fMRI sessions with the same n-back task. Behavioral and brain effects were studied, comparing the conditions and sessions in both groups. Our results showed that n-back training improved performance in terms of accuracy and response speed in the trained group compared to the control group. These behavioral changes in trained participants were associated with decreased activation in various brain areas related to working memory, specifically the frontal superior/middle cortex, inferior parietal cortex, anterior cingulate cortex, and middle temporal cortex. Five weeks after training, the behavioral and brain changes remained stable. We conclude that cognitive training was associated with an improvement in behavioral performance and decreased brain activation, suggesting better neural efficiency that persists over time.

Internet gaming disorder: deficits in functional and structural connectivity in the ventral tegmental area-Accumbens pathway Abstract Dopamine projections from the ventral tegmental area (VTA) to the nucleus accumbens (NAc) and from the substantia nigra (SN) to the dorsal striatum are involved in addiction. However, relatively little is known about the implication of these circuits in Internet gaming disorder (IGD). This study examined the alteration of resting-state functional connectivity (RSFC) and diffusion tensor imaging (DTI) -based structural connectivity of VTA/SN circuits in 61 young male participants (33 IGD and 28 healthy controls). Correlation analysis was carried out to investigate the relationship between the neuroimaging findings and the behavioral Internet Addiction Test (IAT). Both the NAc and medial orbitofrontal cortex (mOFC) showed lower RSFC with VTA in IGD subjects compared with controls. Moreover, the RSFC strength of VTA-right NAc and VTA-left mOFC correlated negatively with IAT in IGD subjects. The IGD subjects also showed lower structural connectivity in bilateral VTA-NAc tracts compared with controls, but the connectivity did not correlate with IAT in IGD. We provide evidence that functional and structural connectivity of the VTA-NAc pathway, and functional connectivity of the VTA-mOFC pathway are implicated in IGD. Since these pathways are important for dopamine reward signals and salience attribution, the findings suggest involvement of the brain DA reward system in the neurobiology of IGD. The association of functional but not structural connectivity of VTA circuits with IAT suggests that while lower structural connectivity might underlie vulnerability for IGD, lower functional connectivity may modulate severity. These results strengthen the evidence that IGD shares similar neuropathology with other addictions.

12-h abstinence-induced functional connectivity density changes and craving in young smokers: a resting-state study Abstract Studying the neural correlates of craving to smoke is of great importance to improve treatment outcomes in smoking addiction. According to previous studies, the critical roles of striatum and frontal brain regions had been revealed in addiction. However, few studies focused on the hub of brain regions in the 12 h abstinence induced craving in young smokers. Thirty-one young male smokers were enrolled in the present study. A within-subject experiment design was carried out to compare functional connectivity density between 12-h smoking abstinence and smoking satiety conditions during resting state in young adult smokers by using functional connectivity density mapping (FCDM). Then, the functional connectivity density changes during smoking abstinence versus satiety were further used to examine correlations with abstinence-induced changes in subjective craving. We found young adult smokers in abstinence state (vs satiety) had higher local functional connectivity density (lFCD) and global functional connectivity density (gFCD) in brain regions including striatal subregions (i.e., bilateral caudate and putamen), frontal regions (i.e., anterior cingulate cortex (ACC) and orbital frontal cortex (OFC)) and bilateral insula. We also found higher lFCD during smoking abstinence (vs satiety) in bilateral thalamus. Additionally, the lFCD changes of the left ACC, bilateral caudate and right OFC were positively correlated with the changes in craving induced by abstinence (i.e., abstinence minus satiety) in young adult smokers. The present findings improve the understanding of the effects of acute smoking abstinence on the hubs of brain gray matter in the abstinence-induces craving and may contribute new insights into the neural mechanism of abstinence-induced craving in young smokers in smoking addiction.

Hippocampal sulcal cavities: prevalence, risk factors and association with cognitive performance. The SMART-Medea study and PREDICT-MR study Abstract Hippocampal sulcal cavities (HSCs) are frequently observed on MRI, but their etiology and relevance is unclear. HSCs may be anatomical variations, or result from pathology. We assessed the presence of HSCs, and their cross-sectional association with demographics, vascular risk factors and cognitive functioning in two study samples. Within a random sample of 92 patients with vascular disease from the SMART-Medea study (mean age = 62, SD = 9 years) and 83 primary care patients from the PREDICT-MR study (mean age = 62, SD = 12 years) one rater manually scored HSCs at 1.5 T 3D T1-weighted coronal images blind to patient information. We estimated relative risks of age, sex and vascular risk factors with presence of HSCs using Poisson regression with log-link function and robust standard errors adjusted for age and sex. Using ANCOVA adjusted for age, sex, and education we estimated the association of the number of HSCs with memory, executive functioning, speed, and working memory. In the SMART-Medea study HSCs were present in 65% and in 52% in the PREDICT-MR study (χ2 = 2.99, df = 1, p = 0.08). In both samples, no significant associations were observed between presence of HSCs and age (SMART-Medea: RR = 1.00; 95%CI 0.98–1.01; PREDICT-MR: RR = 1.01; 95%CI 0.99–1.03), sex, or vascular risk factors. Also, no associations between HSCs and cognitive functioning were found in either sample. HSCs are frequently observed on 1.5 T MRI. Our findings suggest that, in patients with a history of vascular disease and primary care attendees, HSCs are part of normal anatomic variation of the human hippocampus rather than markers of pathology.