Convergent Approaches for Defining Functional Imaging Endophenotypes in Schizophrenia

In complex genetic disorders such as schizophrenia, endophenotypes have potential utility both in identifying risk genes and in illuminating pathophysiology. This is due to their presumed status as closer in the etiopathological pathway to the causative genes than is the currently defining clinical phenomenology of the illness and thus their simpler genetic architecture than that of the full syndrome. There, many genes conferring slight individual risk are additive or epistatic (interactive) with regard to cumulative schizophrenia risk. In addition the use of endophenotypes has encouraged a conceptual shift away from the exclusive study of categorical diagnoses in manifestly ill patients, towards the study of quantitative traits in patients, unaffected relatives and healthy controls. A more recently employed strategy is thus to study unaffected first-degree relatives of schizophrenia patients, who share some of the genetic diathesis without illness-related confounds that may themselves impact fMRI task performance. Consistent with the multiple biological abnormalities associated with the disorder, many candidate endophenotypes have been advanced for schizophrenia, including measures derived from structural brain imaging, EEG, sensorimotor integration, eye movements and cognitive performance (Allen et al., 2009), but recent data derived from quantitative functional brain imaging measures present additional attractive putative endophenotypes. We will review two major, conceptually different approaches that use fMRI in this context. One, the dominant paradigm, employs defined cognitive tasks on which schizophrenia patients perform poorly as “cognitive stress tests”. The second uses very simple probes or “task-free” approaches where performance in patients and controls is equal. We explore the potential advantages and disadvantages of each method, the associated data analytic approaches and recent studies exploring their interface with the genetic risk architecture of schizophrenia

Evidence for Multiple Manipulation Processes in Prefrontal Cortex

The prefrontal cortex (PFC) is known to subserve working memory (WM) processes. Brain imaging studies of WM using delayed response tasks (DRTs) have shown memory-load-dependent activation increases in dorsal prefrontal cortex (PFC) regions. These activation increases are believed to reflect manipulation of to-be-remembered information in the service of memory-consolidation. This speculation has been based on observations of similar activation increases in tasks that overtly require manipulation by instructing participants to reorder to-be-remembered list items. In this study, we tested the assumption of functional equivalence between these two types of WM tasks. Participants performed a DRT under two conditions with memory loads ranging from 3 to 6 letters. In an “item-order” condition, participants were required to remember letters in the order in which they were presented. In a “reordering” condition, participants were required to remember the letters in alphabetical order. Load-related activation increases were observed during the encoding and maintenance periods of the order maintenance condition, whereas load-related activation decreases were observed in the same periods of the reordering condition. These results suggest that (1) the neural substrates associated with long-list retention and those associated with reordering are not equivalent, (2) cognitive processes associated with long-list retention may be more closely approximated by item-order maintenance than by reordering, and (3) multiple forms of WM manipulation are dissociable on the basis of fMRI data

Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Mapping inter-subject and inter-regional brain connectivity during free viewing of novel natural scenes

Traditional functional magnetic resonance imaging (fMRI) studies have used controlled tasks such as finger tapping to isolate function in distinct cortical. Recent studies have examined the mechanisms involved during natural conditions by asking subjects to freely view the presentation of a movie. The objective of our study was to further observe the extent to which similarities are present between subjects during natural vision. It was hypothesized that there would be a linear relationship between the percentage of region-specific overlap, which is the percent of the anatomical region of interest which contains spatial activation exhibited by all six subjects, and corresponding temporal correlation values between subjects from those regions. In this study, a controlled experiment was conducted in which all the subjects viewed a movie clip from the 2005 thriller Redeye for the first time during the tMRI scan. Spatial and temporal correlations were examined during a forty minute movie clip in which subjects casually viewed the stimulus. Significant spatial overlap between the six scanned subjects was observed in many regions during the viewing of the forty minute stimulus and this overlap was considerably lower during the second ten minute viewing. Temporal correlation values as high as 0.8 were observed between subjects during the viewing of the forty minute clip. Interregional correlation was also examined within subjects. The use of a movie clip allowed for the activation of a numerous functional regions in a single duration to identify similarity in cortical activation during a complex stimulus both spatially and temporally

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Practice Induces Function-Specific Changes in Brain Activity

Practice can have a profound effect on performance and brain activity, especially if a task can be automated. Tasks that allow for automatization typically involve repeated encoding of information that is paired with a constant response. Much remains unknown about the effects of practice on encoding and response selection in an automated task.To investigate function-specific effects of automatization we employed a variant of a Sternberg task with optimized separation of activity associated with encoding and response selection by means of m-sequences. This optimized randomized event-related design allows for model free measurement of BOLD signals over the course of practice. Brain activity was measured at six consecutive runs of practice and compared to brain activity in a novel task.Prompt reductions were found in the entire cortical network involved in encoding after a single run of practice. Changes in the network associated with response selection were less robust and were present only after the third run of practice.This study shows that automatization causes heterogeneous decreases in brain activity across functional regions that do not strictly track performance improvement. This suggests that cognitive performance is supported by a dynamic allocation of multiple resources in a distributed network. Our findings may bear importance in understanding the role of automatization in complex cognitive performance, as increased encoding efficiency in early stages of practice possibly increases the capacity to otherwise interfering information

Intrinsic Inter-Subject Variability in Functional Neuroimaging: Verification Using Blind Source Separation Features

The holy grail of brain imaging is the identification of a biomarker, which can identify an abnormality that can be used to diagnose disease and track the effectiveness of treatment and disease progression. Typically approaches that search for biomarkers start by identifying mean differences between groups of patients and healthy controls. However, combining data from different subjects and groups to be able to make meaningful inferences is not trivial. The structure of the brain in each individual is unique in size and shape as well as in the relative location of anatomical landmarks (e.g. sulci and gyri). When looking for mean differences in functional images, this issue is exacerbated by the presence of variability in functional localization, i.e. variability in the location of functional regions in the brain. This is notably an important reason to focus on looking for inter-individual differences or variability. Inter-subject variability in neuroimaging experiments is often viewed as noise. The analyses are setup in a manner to ignore this variability assuming that a global spatial normalization brings the data into the same space. Nonetheless, functional activation patterns can be impacted by variability in multiple ways for e.g., there could be spatial variability of the maps or variability in the spectral composition of the timecourses or variability in the connectivity between the activation patterns identified. The overarching problem this thesis seeks to contribute to, is seeking improved measures to quantify biologically significant spatial, spectral and connectivity based variability and to identify associated cognitive or behavioral differences in the distribution of brain networks. We have successfully shown that different (spatial and spectral) measures of variability in blind source separated functional activation patterns underline previously unexplained characteristics that help in discerning schizophrenia patients from healthy controls. Additionally, we show that variance measures in dynamic connectivity between networks in healthy controls can justify relationship between connectivity patterns and executive functioning abilities

Impairments of auditory scene analysis in Alzheimer's disease

Parsing of sound sources in the auditory environment or ‘auditory scene analysis’ is a computationally demanding cognitive operation that is likely to be vulnerable to the neurodegenerative process in Alzheimer’s disease. However, little information is available concerning auditory scene analysis in Alzheimer's disease. Here we undertook a detailed neuropsychological and neuroanatomical characterization of auditory scene analysis in a cohort of 21 patients with clinically typical Alzheimer's disease versus age-matched healthy control subjects. We designed a novel auditory dual stream paradigm based on synthetic sound sequences to assess two key generic operations in auditory scene analysis (object segregation and grouping) in relation to simpler auditory perceptual, task and general neuropsychological factors. In order to assess neuroanatomical associations of performance on auditory scene analysis tasks, structural brain magnetic resonance imaging data from the patient cohort were analysed using voxel-based morphometry. Compared with healthy controls, patients with Alzheimer's disease had impairments of auditory scene analysis, and segregation and grouping operations were comparably affected. Auditory scene analysis impairments in Alzheimer's disease were not wholly attributable to simple auditory perceptual or task factors; however, the between-group difference relative to healthy controls was attenuated after accounting for non-verbal (visuospatial) working memory capacity. These findings demonstrate that clinically typical Alzheimer's disease is associated with a generic deficit of auditory scene analysis. Neuroanatomical associations of auditory scene analysis performance were identified in posterior cortical areas including the posterior superior temporal lobes and posterior cingulate. This work suggests a basis for understanding a class of clinical symptoms in Alzheimer's disease and for delineating cognitive mechanisms that mediate auditory scene analysis both in health and in neurodegenerative disease

Effect of Sensory Attenuation on Cortical Movement-Related Oscillations

This study examined the impact of induced sensory deficits on cortical, movement-related oscillations measured using electroencephalography (EEG). We hypothesized that EEG patterns in healthy subjects with induced sensory reduction would be comparable to EEG found after chronic loss of sensory feedback. EEG signals from 64 scalp locations were measured from 10 healthy subjects. Participants dorsiflexed their ankle after prolonged vibration of the tibialis anterior (TA). Beta band time frequency decompositions were calculated using wavelets and compared across conditions. Changes in patterns of movement-related brain activity were observed following attenuation of sensory feedback. A significant decrease in beta power of event-related synchronization was associated with simple ankle dorsiflexion after prolonged vibration of the TA. Attenuation of sensory feedback in young, healthy subjects led to a corresponding decrease in beta band synchronization. This temporary change in beta oscillations suggests that these modulations are a mechanism for sensorimotor integration. The loss of sensory feedback found in spinal cord injury patients contributes to changes in EEG signals underlying motor commands. Similar alterations in cortical signals in healthy subjects with reduced sensory feedback implies these changes reflect normal sensorimotor integration after reduced sensory input rather than brain plasticity

On the Use of Independent Component Analysis & Functional Network Connectivity Analysis: Evaluation on Two Distinct Large-Scale Psychopathology Studies

Medical image analysis techniques are becoming ever useful in allowing us to better understand the complexities and constructs of the brain and its functions. These analysis methods have proven to be integral in revealing trends in brain activity within individuals with mental disorders that are distinguishable from what would be considered normal\u27 activity within healthy populations. This has led us to gaining a better understanding of functional connectivity within the brain, especially within populations suffering from mental disorders. Functional magnetic resonance imaging (fMRI) is one of the leading techniques currently being implemented to explore cognitive function and aberrant brain activity resulting from mental illness. Functional connectivity has investigated the associations of spatially-remote neuronal activations in the brain. Independent component analysis (ICA) is the leading analytical method in functional connectivity research and has been extensively implemented in the analysis of fMRI data, allowing us to draw group inferences from that data. However, there is mounting interest in the functional network connectivity (FNC) among components estimated through group ICA. This type of analysis allows us to delve further into the temporal dependencies among components or \u27regions\u27 within the brain. In this thesis, we investigate the implementation of group ICA and FNC analysis on two large-scale psychopathology studies -— the first from a multi-site study involving the comparison of schizophrenia patients with healthy controls using a sensorimotor task paradigm, and the second from an investigation in psychopathy in prisoners performing an auditory oddball task. In both studies, we analyzed the fMRI data with group ICA and implemented FNC analysis on the resulting ICA output. The purpose of these studies was to investigate differences in modulation of task-related and default mode networks, identifying any potential temporal dependencies among selected components. Our ultimate objective was to demonstrate the effective application of group ICA and FNC analysis together on two large-scale studies of two, distinct psychopathologies. The results of this combined method of analysis establish the practicality and general applicability of group ICA-FNC analysis in the growing fields of functional connectivity and functional network connectivity.\u2