Altered functional connectivity within the central reward network in overweight and obese women.

Background/objectivesNeuroimaging studies in obese subjects have identified abnormal activation of key regions of central reward circuits, including the nucleus accumbens (NAcc), in response to food-related stimuli. We aimed to examine whether women with elevated body mass index (BMI) show structural and resting state (RS) functional connectivity alterations within regions of the reward network.Subjects/methodsFifty healthy, premenopausal women, 19 overweight and obese (high BMI=26-38 kg m(-2)) and 31 lean (BMI=19-25 kg m(-2)) were selected from the University of California Los Angeles' Oppenheimer Center for Neurobiology of Stress database. Structural and RS functional scans were collected. Group differences in grey matter volume (GMV) of the NAcc, oscillation dynamics of intrinsic brain activity and functional connectivity of the NAcc to regions within the reward network were examined.ResultsGMV of the left NAcc was significantly greater in the high BMI group than in the lean group (P=0.031). Altered frequency distributions were observed in women with high BMI compared with lean group in the left NAcc (P=0.009) in a medium-frequency (MF) band, and in bilateral anterior cingulate cortex (ACC) (P=0.014, <0.001) and ventro-medial prefrontal cortex (vmPFC) (P=0.034, <0.001) in a high-frequency band. Subjects with high BMI had greater connectivity of the left NAcc with bilateral ACC (P=0.024) and right vmPFC (P=0.032) in a MF band and with the left ACC (P=0.03) in a high frequency band.ConclusionsOverweight and obese women in the absence of food-related stimuli show significant structural and functional alterations within regions of reward-related brain networks, which may have a role in altered ingestive behaviors

An integrated brain-behavior model for working memory.

Working memory (WM) is a central construct in cognitive neuroscience because it comprises mechanisms of active information maintenance and cognitive control that underpin most complex cognitive behavior. Individual variation in WM has been associated with multiple behavioral and health features including demographic characteristics, cognitive and physical traits and lifestyle choices. In this context, we used sparse canonical correlation analyses (sCCAs) to determine the covariation between brain imaging metrics of WM-network activation and connectivity and nonimaging measures relating to sensorimotor processing, affective and nonaffective cognition, mental health and personality, physical health and lifestyle choices derived from 823 healthy participants derived from the Human Connectome Project. We conducted sCCAs at two levels: a global level, testing the overall association between the entire imaging and behavioral-health data sets; and a modular level, testing associations between subsets of the two data sets. The behavioral-health and neuroimaging data sets showed significant interdependency. Variables with positive correlation to the neuroimaging variate represented higher physical endurance and fluid intelligence as well as better function in multiple higher-order cognitive domains. Negatively correlated variables represented indicators of suboptimal cardiovascular and metabolic control and lifestyle choices such as alcohol and nicotine use. These results underscore the importance of accounting for behavioral-health factors in neuroimaging studies of WM and provide a neuroscience-informed framework for personalized and public health interventions to promote and maintain the integrity of the WM network

Resting-state abnormalities in heroin-dependent individuals

Drug addiction is a major health problem worldwide. Recent neuroimaging studies have shed light into the underlying mechanisms of drug addiction as well as its consequences to the human brain. The most vulnerable, to heroin addiction, brain regions have been reported to be specific prefrontal, parietal, occipital, and temporal regions, as well as, some subcortical regions. The brain regions involved are usually linked with reward, motivation/drive, memory/learning, inhibition as well as emotional control and seem to form circuits that interact with each other. So, along with neuroimaging studies, recent advances in resting-state dynamics might allow further assessments upon the multilayer complexity of addiction. In the current manuscript, we comprehensively review and discuss existing resting-state neuroimaging findings classified into three overlapping and interconnected groups: functional connectivity alterations, structural deficits and abnormal topological properties. Moreover, behavioral traits of heroin-addicted individuals as well as the limitations of the currently available studies are also reviewed. Finally, in need of a contemporary therapy a multimodal therapeutic approach is suggested using classical treatment practices along with current neurotechonologies, such as neurofeedback and goal-oriented video-games

FMRI resting slow fluctuations correlate with the activity of fast cortico-cortical physiological connections

Recording of slow spontaneous fluctuations at rest using functional magnetic resonance imaging (fMRI) allows distinct long-range cortical networks to be identified. The neuronal basis of connectivity as assessed by resting-state fMRI still needs to be fully clarified, considering that these signals are an indirect measure of neuronal activity, reflecting slow local variations in de-oxyhaemoglobin concentration. Here, we combined fMRI with multifocal transcranial magnetic stimulation (TMS), a technique that allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections. We investigated whether the physiological properties of parieto-frontal circuits mapped with short-latency multifocal TMS at rest may have some relationship with the resting-state fMRI measures of specific resting-state functional networks (RSNs). Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network, i.e., the attention system believed to be involved in reorientation of attention. We conclude that resting-state fMRI ongoing slow fluctuations likely reflect the interaction of underlying physiological cortico-cortical connections

Intrinsic Frontolimbic Connectivity and Mood Symptoms in Young Adult Cannabis Users.

Objective: The endocannbinoid system and cannabis exposure has been implicated in emotional processing. The current study examined whether regular cannabis users demonstrated abnormal intrinsic (a.k.a. resting state) frontolimbic connectivity compared to non-users. A secondary aim examined the relationship between cannabis group connectivity differences and self-reported mood and affect symptoms. Method: Participants included 79 cannabis-using and 80 non-using control emerging adults (ages of 18-30), balanced for gender, reading ability, and age. Standard multiple regressions were used to predict if cannabis group status was associated with frontolimbic connectivity after controlling for site, past month alcohol and nicotine use, and days of abstinence from cannabis. Results: After controlling for research site, past month alcohol and nicotine use, and days of abstinence from cannabis, cannabis users demonstrated significantly greater connectivity between left rACC and the following: right rACC (p = 0.001; corrected p = 0.05; f 2 = 0.55), left amygdala (p = 0.03; corrected p = 0.47; f 2 = 0.17), and left insula (p = 0.03; corrected p = 0.47; f 2 = 0.16). Among cannabis users, greater bilateral rACC connectivity was significantly associated with greater subthreshold depressive symptoms (p = 0.02). Conclusions: Cannabis using young adults demonstrated greater connectivity within frontolimbic regions compared to controls. In cannabis users, greater bilateral rACC intrinsic connectivity was associated with greater levels of subthreshold depression symptoms. Current findings suggest that regular cannabis use during adolescence is associated with abnormal frontolimbic connectivity, especially in cognitive control and emotion regulation regions

Methods and Applications of Multivariate Pattern Analysis in Functional MRI Data Analysis.

In spite of the tremendous advances in science and technology, the human brain and its functions are still not completely understood. Functional magnetic resonance imaging (fMRI) is an imaging modality that allows for non-invasive study of brain function and physiology. Thus, fMRI has found many applications in various fields involved in the study of cognition, psychology, psychiatry, neuroscience, etc. Machine learning techniques have gained tremendous interest in recent times for fMRI data analysis. These methods involve learning from numerous examples and then making predictions for new unseen examples. This work addresses the use of machine learning techniques to find and study multivariate patterns in the fMRI brain data. The two main applications explored in this work include temporal brain-state prediction and subject categorization. The within-subject brain-state prediction setup has been used to compare and contrast three different acquisition techniques in a motor-visual activation study. It has also been implemented to highlight the differences in pain regulation networks in healthy controls and subjects with temporomandibular disorders. Lastly, regression has been used to predict graded fMRI activation on a continuous scale in a motor activation and craving study. The between-subject categorization setup has been used to distinguish between patients with Asperger's disorder and healthy controls. A major contribution of our work involves a novel multi-subject machine learning framework. This technique helps to learn a model which is based on information acquired from multiple other subjects' data in addition to the subject's own data. This has been used to classify the craving and non-craving brain states of nicotine-dependent subjects, allowing examination of both population-wide as well as subject-specific neural correlates of nicotine craving. A real-time neurofeedback setup was implemented to provide feedback to a subject using their own brain activation data. Subjects can then be trained to self-regulate their own brain activation.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111357/1/ysshah_1.pd

Resting-state hyperconnectivity of the anticorrelated intrinsic networks in schizophrenic patients and their unaffected siblings

Abnormal connectivity of the intrinsic anticorrelated networks, the task-negative network (TNN) and task-positive network (TPN), is implicated in schizophrenia. Comparisons between schizophrenic patients and their unaffected siblings offer an opportunity to further understand illness susceptibility and pathophysiology. We hypothesized that schizophrenic patients would demonstrate hyperconnectivity in the intrinsic networks and that similar, but less pronounced, hyperconnectivity would be evident in the networks of the unaffected siblings. Resting-state functional magnetic resonance images were obtained from schizophrenic patients (n=25), their unaffected siblings (n=25), and healthy controls (n=25). The posterior cingulate cortex/precuneus (PCC/PCu) and right dorsolateral prefrontal cortex (DLPFC) were used as seed regions to identify the TNN and TPN. Interregional connectivity strengths were analyzed using overlapped intrinsic networks composed of regions common to the intrinsic networks of the three subject groups. In the TNN, schizophrenic patients alone demonstrated hyperconnectivity between the PCC/PCu and left inferior temporal gyrus and between the ventral medial prefrontal cortex and the right lateral parietal cortex. Both schizophrenic patients and their unaffected siblings showed increased connectivity in the TNN between the bilateral inferior temporal gyri. In the TPN, schizophrenic patients showed hyperconnectivity between the left DLPFC and right inferior frontal gyrus relative to unaffected siblings, though this trend only approached statistical significance in comparison to healthy controls. Resting-state hyperconnectivity of the intrinsic networks may underlie the pathophysiology of schizophrenia by disrupting network coordination. Similar, though milder, hyperconnectivity in unaffected siblings of schizophrenic patients may contribute to their cognitive deficits and increased risk to develop schizophrenia

Pre- and post-task resting-state differs in clinical populations

Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection