Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods

Decoding Developmental Differences and Individual Variability in Response Inhibition Through Predictive Analyses Across Individuals

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9–30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences

Memory systems in schizophrenia: Modularity is preserved but deficits are generalized

OBJECTIVE: Schizophrenia patients exhibit impaired working and episodic memory, but this may represent generalized impairment across memory modalities or performance deficits restricted to particular memory systems in subgroups of patients. Furthermore, it is unclear whether deficits are unique from those associated with other disorders. METHOD: Healthy controls (n=1101) and patients with schizophrenia (n=58), bipolar disorder (n=49) and attention-deficit-hyperactivity-disorder (n=46) performed 18 tasks addressing primarily verbal and spatial episodic and working memory. Effect sizes for group contrasts were compared across tasks and the consistency of subjects\u27 distributional positions across memory domains was measured. RESULTS: Schizophrenia patients performed poorly relative to the other groups on every test. While low to moderate correlation was found between memory domains (r=.320), supporting modularity of these systems, there was limited agreement between measures regarding each individual\u27s task performance (ICC=.292) and in identifying those individuals falling into the lowest quintile (kappa=0.259). A general ability factor accounted for nearly all of the group differences in performance and agreement across measures in classifying low performers. CONCLUSIONS: Pathophysiological processes involved in schizophrenia appear to act primarily on general abilities required in all tasks rather than on specific abilities within different memory domains and modalities. These effects represent a general shift in the overall distribution of general ability (i.e., each case functioning at a lower level than they would have if not for the illness), rather than presence of a generally low-performing subgroup of patients. There is little evidence that memory impairments in schizophrenia are shared with bipolar disorder and ADHD

Predicting risky choices from brain activity patterns

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights

Neurocognitive and Neuroimaging Predictors of Clinical Outcome in Bipolar Disorder

Historically, bipolar disorder has been conceptualized as a disease involving episodic rather than chronic dysfunction. However, increasing evidence indicates that bipolar disorder is associated with substantial inter-episode psychosocial and vocational impairment. Here we review the contributions of neurocognitive deficits and structural and functional neuroanatomic alterations to the observed functional impairments. In particular, compelling evidence now suggests that neurocognitive impairments, particularly in the areas of attention, processing speed, and memory, are associated with functional outcome. Although investigation of the neural correlates of functional disability in bipolar disorder is only in its nascent stages, preliminary evidence suggests that white matter abnormalities may be predictive of poor outcome. A better understanding of the relationship between neurocognitive and neuroimaging assays and functional outcome has the potential to improve current treatment options and provide targets for new treatment strategies in bipolar disorder

Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study

Background and aims: Familial hypercholesterolemia (FH) is an inherited disorder characterized by high levels of blood cholesterol from birth and premature coronary heart disease. Thus, the identification of FH patients is crucial to prevent or delay the onset of cardiovascular events, and the availability of a tool helping with the diagnosis in the setting of general medicine is essential to improve FH patient identification.Methods: This study evaluated the performance of the Dutch Lipid Clinic Network (DLCN) score in FH patients enrolled in the LIPIGEN study, an Italian integrated network aimed at improving the identification of patients with genetic dyslipidaemias, including FH.Results: The DLCN score was applied on a sample of 1377 adults (mean age 42.9 +/- 14.2 years) with genetic diagnosis of FH, resulting in 28.5% of the sample classified as probable FH and 37.9% as classified definite FH. Among these subjects, 43.4% had at least one missing data out of 8, and about 10.0% had 4 missing data or more. When analyzed based on the type of missing data, a higher percentage of subjects with at least 1 missing data in the clinical history or physical examination was classified as possible FH (DLCN score 3-5). We also found that using real or estimated pre-treatment LDL-C levels may significantly modify the DLCN score.Conclusions: Although the DLCN score is a useful tool for physicians in the diagnosis of FH, it may be limited by the complexity to retrieve all the essential information, suggesting a crucial role of the clinical judgement in the identification of FH subjects

GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function : a report from the COGENT consortium

CORRIGENDUM Molecular Psychiatry (2017) 22, 1651–1652 http://www.nature.com/articles/mp2017197.pdfThe complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (similar to 8M single-nucleotide polymorphisms (SNP) with minor allele frequency >= 1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (PPeer reviewe

Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

Intelligence is highly heritable(1) and a major determinant of human health and well-being(2). Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.Peer reviewe

Cortisol, cognition and the ageing prefrontal cortex

The structural and functional decline of the ageing human brain varies by brain region, cognitive function and individual. The underlying biological mechanisms are poorly understood. One potentially important mechanism is exposure to glucocorticoids (GCs; cortisol in humans); GC production is increasingly varied with age in humans, and chronic exposure to high levels is hypothesised to result in cognitive decline via cerebral remodelling. However, studies of GC exposure in humans are scarce and methodological differences confound cross-study comparison. Furthermore, there has been little focus on the effects of GCs on the frontal lobes and key white matter tracts in the ageing brain. This thesis therefore examines relationships among cortisol levels, structural brain measures and cognitive performance in 90 healthy, elderly community-dwelling males from the Lothian Birth Cohort 1936. Salivary cortisol samples characterised diurnal (morning and evening) and reactive profiles (before and after a cognitive test battery). Structural variables comprised Diffusion Tensor Imaging measures of major brain tracts and a novel manual parcellation method for the frontal lobes. The latter was based on a systematic review of current manual methods in the context of putative function and cytoarchitecture. Manual frontal lobe brain parcellation conferred greater spatial and volumetric accuracy when compared to both single- and multi-atlas parcellation at the lobar level. Cognitive ability was assessed via tests of general cognitive ability, and neuropsychological tests thought to show differential sensitivity to the integrity of frontal lobe sub-regions. The majority of, but not all frontal lobe test scores shared considerable overlap with general cognitive ability, and cognitive scores correlated most consistently with the volumes of the anterior cingulate. This is discussed in light of the diverse connective profile of the cingulate and a need to integrate information over more diffuse cognitive networks according to proposed de-differentiation or compensation in ageing. Individuals with higher morning, evening or pre-test cortisol levels showed consistently negative relationships with specific regional volumes and tract integrity. Participants whose cortisol levels increased between the start and end of cognitive testing showed selectively larger regional volumes and lower tract diffusivity (correlation magnitudes <.44). The significant relationships between cortisol levels and cognition indicated that flatter diurnal slopes or higher pre-test levels related to poorer test performance. In contrast, higher levels in the morning generally correlated with better scores (correlation magnitudes <.25). Interpretation of all findings was moderated by sensitivity to type I error, given the large number of comparisons conducted. Though there were limited candidates for mediation analysis, cortisol-function relationships were partially mediated by tract integrity (but not sub-regional frontal volumes) for memory and post-error slowing. This thesis offers a novel perspective on the complex interplay among glucocorticoids, cognition and the structure of the ageing brain. The findings suggest some role for cortisol exposure in determining age-related decline in complex cognition, mediated via brain structure