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Separate neural pathways process different decision costs.
Behavioral ecologists and economists emphasize that potential costs, as well as rewards, influence decision making. Although neuroscientists assume that frontal areas are central to decision making, the evidence is contradictory and the critical region remains unclear. Here it is shown that frontal lobe contributions to cost-benefit decision making can be understood by positing the existence of two independent systems that make decisions about delay and effort costs. Anterior cingulate cortex lesions affected how much effort rats decided to invest for rewards. Orbitofrontal cortical lesions affected how long rats decided to wait for rewards. The pattern of disruption suggested the deficit could be related to impaired associative learning. Impairments of the two systems may underlie apathetic and impulsive choice patterns in neurological and psychiatric illnesses. Although the existence of two systems is not predicted by economic accounts of decision making, our results suggest that delay and effort may exert distinct influences on decision making.
Assessing psychiatric comorbid disorders of cognition: A machine learning approach using UK Biobank data
<jats:p>Background: Using UK Biobank data from baseline to the imaging study, our aim was to investigate whether participants with comorbid psychiatric disorders suffer from higher risk of impaired cognition over time compared to those without. There are several conventional statistical techniques to examine whether this pattern indicates the increased risk of comorbidity of the aforementioned, however, they carry certain inherent limitations. Machine learning (ML) has shown specific advantages in examining potential predictors simultaneously in an unbiased manner, especially its ability to identify patterns of information within useful features for the prediction of an outcome of interest. We applied ML techniques on UK Biobank data to examine whether anxiety and/or depression are important longitudinal predictors of impaired cognition. Methods: We used data from UK Biobank (n = 1.158) across three time waves to longitudinally assess the effects of comorbidity (anxiety and depression, measured through self-report scales) on cognition. Theta (Θ) measures for each mental health scale were computed using item response theory (IRT) and intraindividual variability of reaction time performance (IIV) - raw standard deviation - was used as a measure of cognitive performance. First, comorbidity information was summarized to show the variation of impaired cognition for participants with and without psychiatric disorders. A machine learning (ML) approach was then applied to examine if psychiatric disorders and other putative covariate markers may be important predictors of long-term cognitive decline. Results: For participants with anxiety, the percentage of having impaired cognition constantly increases through time from 35.09% to 42.15%; in contrast, an increase with less magnitude is found for participants without anxiety from 36.10% to 40.03%. Likewise, for participants with depression, the percentage of having impaired cognition constantly increases through time from 35.49% to 41.67%; in contrast, an increase with less magnitude is found for participants without depression from 35.64% to 40.52%. Using the area under the Receiver Operating Characteristic (ROC) curve it was observed that the anxiety model achieved the best performance among all models, with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.64. The cardiovascular and diabetes model and the demographics model had relatively weak performance in predicting cognition, with an AUC of 0.60 and 0.57, respectively. Conclusions: Using data from UK Biobank, this study provides empirical evidence which suggests that psychiatric disorders are important comorbidities of cognitive decline. Furthermore, when other comorbidities were included in the model these were not as important on long-term effect. When the recurrent neural networks were trained for the psychiatric disorder features (anxiety and depression) they showed improved performance in predicting cognition in comparison with cardiovascular disease, diabetes and demographic factors. These findings suggest that mental health disorders (anxiety and depression) have a deleterious effect on long-term cognition, and may be considered an important comorbid disorder of cognitive decline. The implications of this work is that the important predictive effect of poor mental health on longitudinal cognitive decline should be considered in both research and clinical settings.</jats:p>
D-Cycloserine as Adjunct to Brief Computerised CBT for Spider Fear: Effects on Fear, Behaviour, and Cognitive Biases
Abstract In anxiety disorders, cognitive behavioural therapy (CBT) improves information-processing biases such as implicit fear evaluations and avoidance tendencies, which predicts treatment response, so they might constitute important treatment targets. This study investigated (i) whether information-processing biases changed following single-session computerised CBT for spider fear, and (ii) whether this effect could be augmented by administration of D-cycloserine (DCS). Spider-fearful individuals were randomized to receiving 250mg of DCS (n=21) or placebo (n=17) and spider fear was assessed using self-report, behavioural, and information-processing (Extrinsic Affective Simon Task & Approach Avoidance Task) measures. Linear mixed-effects analyses indicated improvements on self-report and behavioural spider fear following CBT, but not on cognitive bias measures. There was no evidence of an augmentation effect of DCS on any outcome. Cognitive biases at 1-day were not predictive of 1-month follow-up spider fear. These findings provide no evidence for information-processing biases relating to CBT response or augmentation with DCS.
A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex.
Although the ventromedial prefrontal cortex (vmPFC) has long been implicated in reward-guided decision making, its exact role in this process has remained an unresolved issue. Here we show that, in accordance with models of decision making, vmPFC concentrations of GABA and glutamate in human volunteers predict both behavioral performance and the dynamics of a neural value comparison signal. These data provide evidence for a neural competition mechanism in vmPFC that supports value-guided choice.
Mechanisms underlying cortical activity during value-guided choice.
When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity that is fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. We investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions using magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.
Deep clinical and biological phenotyping of the preterm birth and small for gestational age syndromes: The INTERBIO-21 st Newborn Case-Control Study protocol.
Background: INTERBIO-21 st is Phase II of the INTERGROWTH-21 st Project, the population-based, research initiative involving nearly 70,000 mothers and babies worldwide coordinated by Oxford University and performed by a multidisciplinary network of more than 400 healthcare professionals and scientists from 35 institutions in 21 countries worldwide. Phase I, conducted 2008-2015, consisted of nine complementary studies designed to describe optimal human growth and neurodevelopment, based conceptually on the WHO prescriptive approach. The studies generated a set of international standards for monitoring growth and neurodevelopment, which complement the existing WHO Child Growth Standards. Phase II aims to improve the functional classification of the highly heterogenous preterm birth and fetal growth restriction syndromes through a better understanding of how environmental exposures, clinical conditions and nutrition influence patterns of human growth from conception to childhood, as well as specific neurodevelopmental domains and associated behaviors at 2 years of age. Methods: In the INTERBIO-21 st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles. Here we describe the study sites, population characteristics, study design, methodology and standardization procedures for the collection of longitudinal clinical data and biological samples (maternal blood, umbilical cord blood, placental tissue, maternal feces and infant buccal swabs) for the study that was conducted between 2012 and 2018 in Brazil, Kenya, Pakistan, South Africa, Thailand and the UK. Discussion: Our study provides a unique resource for the planned analyses given the range of potentially disadvantageous exposures (including poor nutrition, pregnancy complications and infections) in geographically diverse populations worldwide. The study should enhance current medical knowledge and provide new insights into environmental influences on human growth and neurodevelopment.
Spectrally resolved fast transient brain states in electrophysiological data.
The brain is capable of producing coordinated fast changing neural dynamics across multiple brain regions in order to adapt to rapidly changing environments. However, it is non-trivial to identify multiregion dynamics at fast sub-second time-scales in electrophysiological data. We propose a method that, with no knowledge of any task timings, can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties. The approach models brain activity using a discrete set of sequential states, with each state distinguished by its own multiregion spectral properties. This can identify potentially very short-lived visits to a brain state, at the same time as inferring the state's properties, by pooling over many repeated visits to that state. We show how this can be used to compute state-specific measures such as power spectra and coherence. We demonstrate that this can be used to identify short-lived transient brain states with distinct power and functional connectivity (e.g., coherence) properties in an MEG data set collected during a volitional motor task.
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
Discovering dynamic brain networks from big data in rest and task.
Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.
A positive-negative mode of population covariation links brain connectivity, demographics and behavior.
We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.
Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest.
Growing evidence has shown that brain activity at rest slowly wanders through a repertoire of different states, where whole-brain functional connectivity (FC) temporarily settles into distinct FC patterns. Nevertheless, the functional role of resting-state activity remains unclear. Here, we investigate how the switching behavior of resting-state FC relates with cognitive performance in healthy older adults. We analyse resting-state fMRI data from 98 healthy adults previously categorized as being among the best or among the worst performers in a cohort study of >1000 subjects aged 50+ who underwent neuropsychological assessment. We use a novel approach focusing on the dominant FC pattern captured by the leading eigenvector of dynamic FC matrices. Recurrent FC patterns - or states - are detected and characterized in terms of lifetime, probability of occurrence and switching profiles. We find that poorer cognitive performance is associated with weaker FC temporal similarity together with altered switching between FC states. These results provide new evidence linking the switching dynamics of FC during rest with cognitive performance in later life, reinforcing the functional role of resting-state activity for effective cognitive processing.
Brain network dynamics are hierarchically organized in time.
The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.
Functional connectomics from resting-state fMRI.
Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.
Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment.
Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.
A Survey of L Regression
L regularization, or regularization with an L penalty, is a popular idea in statistics and machine learning. This paper reviews the concept and application of L regularization for regression. It is not our aim to present a comprehensive list of the utilities of the L penalty in the regression setting. Rather, we focus on what we believe is the set of most representative uses of this regularization technique, which we describe in some detail. Thus, we deal with a number of L-regularized methods for linear regression, generalized linear models, and time series analysis. Although this review targets practice rather than theory, we do give some theoretical details about L-penalized linear regression, usually referred to as the least absolute shrinkage and selection operator (lasso). © 2013 International Statistical Institute.
Sparse regularized local regression
The intention is to provide a Bayesian formulation of regularized local linear regression, combined with techniques for optimal bandwidth selection. This approach arises from the idea that only those covariates that are found to be relevant for the regression function should be considered by the kernel function used to define the neighborhood of the point of interest. However, the regression function itself depends on the kernel function. A maximum posterior joint estimation of the regression parameters is given. Also, an alternative algorithm based on sampling techniques is developed for finding both the regression parameter distribution and the predictive distribution. © 2013 Elsevier B.V. All rights reserved.
Sparse regularized local regression
The intention is to provide a Bayesian formulation of regularized local linear regression, combined with techniques for optimal bandwidth selection. This approach arises from the idea that only those covariates that are found to be relevant for the regression function should be considered by the kernel function used to define the neighborhood of the point of interest. However, the regression function itself depends on the kernel function. A maximum posterior joint estimation of the regression parameters is given. Also, an alternative algorithm based on sampling techniques is developed for finding both the regression parameter distribution and the predictive distribution. © 2013 Elsevier B.V.