Albuquerque, NM, United States
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Vosskuhl J.,University of Oldenburg | Huster R.J.,University of Oldenburg | Huster R.J.,University of Oslo | Huster R.J.,The Mind Research Network | Herrmann C.S.,University of Oldenburg
Frontiers in Human Neuroscience | Year: 2015

Working memory (WM) and short-term memory (STM) supposedly rely on the phase-amplitude coupling (PAC) of neural oscillations in the theta and gamma frequency ranges. The ratio between the individually dominant gamma and theta frequencies is believed to determine an individual’s memory capacity. The aim of this study was to establish a causal relationship between the gamma/theta ratio and WM/STM capacity by means of transcranial alternating current stimulation (tACS). To achieve this, tACS was delivered at a frequency below the individual theta frequency. Thereby the individual ratio of gamma to theta frequencies was changed, resulting in an increase of STM capacity. Healthy human participants (N = 33) were allocated to two groups, one receiving verum tACS, the other underwent a sham control protocol. The electroencephalogram (EEG) was measured before stimulation and analyzed with regard to the properties of PAC between theta and gamma frequencies to determine individual stimulation frequencies. After stimulation, EEG was recorded again in order to find after-effects of tACS in the oscillatory features of the EEG. Measures of STM and WM were obtained before, during and after stimulation. Frequency spectra and behavioral data were compared between groups and different measurement phases. The tACS- but not the sham stimulated group showed an increase in STM capacity during stimulation. WM was not affected in either groups. An increase in task-related theta amplitude after stimulation was observed only for the tACS group. These augmented theta amplitudes indicated that the manipulation of individual theta frequencies was successful and caused the increase in STM capacity. © 2015 Vosskuhl, Huster and Herrmann.

Calhoun V.D.,The Mind Research Network | Calhoun V.D.,University of New Mexico | Allen E.,The Mind Research Network
Psychometrika | Year: 2013

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i. e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0. 85 for task data and 0. 65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion. © 2012 The Psychometric Society.

Bernard J.A.,University of Colorado at Boulder | Leopold D.R.,University of Colorado at Boulder | Calhoun V.D.,The Mind Research Network | Calhoun V.D.,University of New Mexico | Mittal V.A.,University of Colorado at Boulder
Human Brain Mapping | Year: 2015

Cerebellar morphology and function have been implicated in a variety of developmental disorders, and in healthy aging. Although recent work has sought to characterize the relationships between volume and age in this structure during adolescence, young, and older adulthood, there have been no investigations of regional cerebellar volume from adolescence through late middle age. Middle age in particular has been largely understudied, and investigating this period of the lifespan may be especially important for our understanding of senescence. Understanding regional patterns of cerebellar volume with respect to age during this portion of the lifespan may provide important insight into healthy aging and cognitive function as well as pathology from adolescence into later life. We investigated regional cerebellar volume using a highly novel lobular segmentation approach in conjunction with a battery of cognitive tasks in a cross-sectional sample of 123 individuals from 12 to 65 years old. Our results indicated that regional cerebellar volumes show different patterns with respect to age. In particular, the more posterior aspect of the neocerebellum follows a quadratic "inverse-U" pattern while the vermis and anterior cerebellum follow logarithmic patterns. In addition, we quantified the relationships between age and a variety of cognitive assessments and found relationships between regional cerebellar volumes and performance. Finally, exploratory analyses of sex differences in the relationships between regional cerebellar volume, age, and cognition were investigated. Taken together, these results provide key insights into the development and aging of the human cerebellum, and its role in cognitive function across the lifespan. Hum Brain Mapp 36:1102-1120, 2015. © 2014 Wiley Periodicals, Inc.

Erhardt E.B.,The Mind Research Network | Rachakonda S.,The Mind Research Network | Bedrick E.J.,University of New Mexico | Allen E.A.,The Mind Research Network | And 3 more authors.
Human Brain Mapping | Year: 2011

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise-free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. © 2010 Wiley Periodicals, Inc.

Sui J.,The Mind Research Network | Sui J.,CAS Institute of Automation | Huster R.,Carl von Ossietzky University | Yu Q.,The Mind Research Network | And 3 more authors.
NeuroImage | Year: 2014

Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques. © 2013 Elsevier Inc.

Prause N.,The Mind Research Network
Sexual and Relationship Therapy | Year: 2011

Orgasm is assumed to be the height of sexual pleasure, reinforcing the recurrence of sexual behaviors. Surprisingly, data supporting the role of orgasm as a reward in women appear lacking. The most likely psychological function of orgasm in women, consistent with the very limited empirical information, is as a secondary reinforcer. In other words, sexual arousal is the primary reward for sexual behavior in women and orgasm associates sexual arousal with the partner. Data from a small (n = 38 women) pilot are presented to highlight the challenges of studying female orgasm. Challenges include differentiating vaginally- or clitorally-generated orgasms by self-report and the large proportion of women who are unsure if they experience orgasms. Finally, the recent spate of publications purporting to show differences in penile-vaginal intercourse induced orgasms is critiqued in light of the information reviewed. © 2011 Copyright Taylor and Francis Group, LLC.

Claus E.D.,The Mind Research Network
Alcoholism, clinical and experimental research | Year: 2012

Impulsivity, particularly risk taking, is believed to play a significant role in alcohol use disorders (AUDs). While risk taking has been measured using questionnaires, recent performance-based tasks such as the Balloon Analog Risk Task (BART) have shown considerable promise in understanding risky decision-making processes in drinkers. While the number of studies using the BART has grown significantly over the past decade, the neural mechanisms that underlie risky choices on the BART have only begun to be explored. The current study was designed to assess both the neural mechanisms of risk taking on the BART and to explore relationships between risk taking and hazardous drinking. Seventy-nine individuals with an AUD completed an fMRI compatible version of the BART that required pumping simulated air into risky or nonrisky balloons to earn points on each trial, and deciding when to terminate pumping to earn points accumulated. Hazardous drinking was assessed with the Alcohol Use Disorder Identification Test (AUDIT). Comparison of risky and nonrisky decisions revealed differences in the dorsal anterior cingulate cortex (dACC), anterior insula, and striatum. Comparison of Cashout responses and Explosions revealed increased responses in lateral prefrontal cortex, insula, ACC, and middle temporal gyrus during Explosions and greater response in inferior parietal lobe and caudate during Cashouts. When examining relationships between hazardous drinking and neural measures of risk taking, we found significant negative relationships with insula, striatum, and dACC. The current results suggest that risk taking is associated with increased response in the dACC and anterior insula, regions previously implicated in representing error likelihood and negative outcome magnitudes, respectively. In addition, hazardous drinking was associated with responses in the dACC, possibly suggesting a reduced ability to predict the likelihood of errors and to predict negative outcomes associated with risk taking. Copyright © 2012 by the Research Society on Alcoholism.

Weiland B.J.,University of Colorado at Boulder | Sabbineni A.,University of Colorado at Boulder | Calhoun V.D.,The Mind Research Network | Calhoun V.D.,University of New Mexico | And 3 more authors.
Human Brain Mapping | Year: 2015

Altered functional connectivity has been associated with acute and chronic nicotine use. Connectivity alterations, specifically in the right and left executive control networks (RECN/LECN) and the default mode network (DMN), may contribute to the addiction cycle. The objective of this study was to determine if executive control network (ECN) and DMN connectivity is different between non-smokers and smokers and whether reductions in connectivity are related to chronic cigarette use. The RECN, LECN, and DMN were identified in resting state functional magnetic resonance imaging data in 650 subjects. Analyses tested for group differences in network connectivity strength, controlling for age and alcohol use. There was a significant group effect on LECN and DMN connectivity strength with smokers (n=452) having lower network strengths than non-smokers (n=198). Smokers had lower connectivity than non-smokers associated with key network hubs: the dorsolateral prefrontal cortex, and parietal nodes within ECNs. Further, ECN connectivity strength was negatively associated with pack years of cigarette use. Our data suggest that chronic nicotine use negatively impacts functional connectivity within control networks that may contribute to the difficulty smokers have in quitting. Hum Brain Mapp 36:872-882, 2015. © 2014 Wiley Periodicals, Inc.

Chen J.,The Mind Research Network
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

Independent component analysis (ICA), a blind source separation method, has been shown to be a useful approach to identify genetic components representing combined effects from multiple mutations. However, the ICA order selection for genotype data has been a challenge, since a genetic component usually accounts for a small amount of variance of the data, and makes it difficult to distinguish true signals from background. To address this issue, we propose to select ICA order based on consistency and implement three strategies in this study. Simulations demonstrate robust performances of all three strategies where the selected orders lead to optimal results regardless of ICA performances.

News Article | November 29, 2016

CORAL GABLES, Fla. (November 16, 2016)--Your brain is never really at rest. Neither is it in chaos. Even when not engaged in some task, the brain naturally cycles through identifiable patterns of neural connections--sort of like always practicing your favorite songs when learning to play the guitar. Constantly cycling through brain region connections may make it easier to call to those networks when you need them for high-level cognitive processing, such as memory and attention. The network connections are not all equal, either. Some are more flexible and adaptable than others. This is what Lucina Uddin and Jason Nomi, cognitive neuroscientists at the University of Miami College of Arts and Sciences, found when collaborating with researchers at the University of New Mexico on a study that researchers hope will lay the groundwork for helping children with autism adapt to change more easily. The scientists analyzed an extensive data set of brain region connectivity from the NIH-funded Human Connectome Project (HCP), which is mapping neural connections in the brain and makes its data publicly available. To better understand the human brain connectome, the HCP collected data from hundreds of people who underwent 56 minutes of resting-state functional magnetic resonance imaging (fMRI). A revolutionary tool in brain-mapping research, fMRIs measure brain activity by detecting changes in cerebral blood flow that are associated with brain activity and neural activation. The HCP also collected a number of other measurements, including the subjects' ages, IQs, and results on various mental tasks. Nomi, Uddin, and their fellow researchers analyzed the HCP's resting-state fMRI data and, from potentially hundreds of configurations, teased apart five general brain patterns. They discovered that, most of the time, neural connections in the typical adult population are agile--alert yet fluid and flexible enough to take on whatever challenges or mental tasks are presented. Less frequently, the brain cycles through more rigid connections where the regions are linked in a very specific, less flexible way, says Uddin, assistant professor of psychology and principal investigator in the Brain Connectivity and Cognition Laboratory (BCCL). The researchers then correlated the frequency of these five brain patterns with performance on executive-function tasks--completed outside of the fMRI brain scanner--that tap high-level cognition, such as sorting a deck of cards by the printed image's color and then by its shape. What they found was higher performers tend to have a natural propensity to be in the more flexible and fluid brain states. "People who do better on these tasks tend to have more of the relaxed, flexible brain configuration states and less of the more rigid configuration states," says Nomi, a postdoctoral fellow in the Department of Psychology and the BCCL. With this better understanding of brain activity in a typical population, the researchers are now moving to the next step of their research: testing children with autism to see whether their brains have a natural propensity to spend more time in the more rigid network configurations, making it harder for them to adapt to change as they experience life. "The final step is determining what can we do to help them do better," Uddin says. "Is there a way to induce a brain state that helps children with autism more flexibly adapt? Are there training programs or behavioral therapies that help them become more flexible? And if there are, do they also help their brains become more flexible?" Uddin, Nomi, and their fellow researchers who study the connection between neuroscience and behavior are excited about the direction neuroimaging has taken their field. "In the field of neuroimaging, before, we would have a snapshot of the brain. Now, we have a movie," says Uddin. Neuroscientists are also making more data publically available, and building interdisciplinary collaborations to analyze big data. Uddin, Nomi, and their collaborators were able to analyze more than 80 gigabytes of data for the connectome study in weeks, rather than months, by using the supercomputing resources at UM's Center for Computational Science (CCS). For the follow up study on children with autism, Uddin and Nomi have been working closely with UM's Michael Alessandri, clinical professor of psychology and executive director of the University of Miami-Nova Southeastern University Center for Autism and Related Disabilities (UM-NSU CARD); Melissa Hale, clinical assistant professor of psychology and UM-NSU-CARD's associate director; and Meaghan Parlade, a licensed psychologist at the Autism Spectrum Assessment Clinic (ASAC) in the Department of Psychology as well as the coordinator of research and training for UM-NSU CARD. The team's UMiami Brain Development Lab is looking for children ages 7 to 12, who are typically developing or who have autism, to help them understand more about how the brain functions in both populations. Parents can learn more by viewing this video. For their research study, "Chronnectomic Patterns and Neural Flexibility Underlie Executive Function," Nomi and Uddin worked with Shruti Gopal Vij, a biomedical engineer and postdoctoral researcher in the Brain Connectivity and Cognition Lab and The Mind Research Network in Albuquerque; Dina Dajani, a graduate student in psychology at UM's College of Arts and Sciences; Rosa Steimke, a visiting postdoctoral researcher in psychology in the Brain Connectivity and Cognition Lab; Eswar Damaraju and Srinivas Rachakonda, of The Mind Research Network; and Vince Calhoun, of The Mind Research Network and the Department of Electrical and Computer Engineering at the University of New Mexico.

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