Multivariate brain-behaviour associations in psychiatric disorders.

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Psychosocial stress, interpersonal sensitivity, and social withdrawal in clinical high risk for psychosis: a systematic review.

Stress has repeatedly been implicated in the onset and exacerbation of positive symptoms of psychosis. Increasing interest is growing for the role of psychosocial stress in the development of psychosis symptoms in individuals at Clinical High Risk (CHR) for psychosis. A systematic review was therefore conducted to summarize the existing evidence base regarding psychosocial stress, interpersonal sensitivity, and social withdrawal in individuals at CHR for psychosis. An electronic search of Ovid (PsychINFO, EMBASE, MEDLINE, and GLOBAL HEALTH) was conducted until February 2022. Studies that examined psychosocial stress in CHR were included. Twenty-nine studies were eligible for inclusion. Psychosocial stress, interpersonal sensitivity, and social withdrawal were higher in CHR individuals compared to healthy controls and there was some evidence of their association with positive symptoms of psychosis. Two types of psychosocial stressors were found to occur more frequently with CHR status, namely daily stressors, and early and recent trauma, while significant life events did not appear to be significant. Greater exposure to psychosocial stress, emotional abuse, and perceived discrimination significantly increased risk of transition to psychosis in CHR. No studies examined the role of interpersonal sensitivity on transition to psychosis in CHR. This systematic review provides evidence for the association of trauma, daily stressors, social withdrawal, and interpersonal sensitivity with CHR status. Further studies investigating the impact of psychosocial stress on psychosis symptom expression in individuals at CHR and its effects on transition to psychosis are therefore warranted.

The association between psychosocial stress, interpersonal sensitivity, social withdrawal and psychosis relapse: a systematic review.

Psychosis is associated with a high risk of relapse, with 67% of clients relapsing within one year following a first episode. In light of the high personal, social, and healthcare costs of the illness, it is paramount to understand the risk factors associated with psychosis relapse. The current systematic review aims to critically review the role of psychosocial stress in psychosis relapse in individuals with an established psychotic disorder. This review systematically searched Ovid (PsycINFO, EMBASE, MEDLINE) literature databases from inception until 28th February 2022. Sixteen studies were eligible for inclusion. Most studies found that individuals with psychosis demonstrate high levels of psychosocial stress and are more likely to be socially withdrawn compared to healthy controls or other clinical presentations. Most studies reported a statistically significant association between psychosocial stress and psychosis relapse, as well as between social withdrawal and psychosis relapse. However, no studies examined the association between high levels of interpersonal sensitivity and psychosis relapse. Individuals with psychosis tend to experience high levels of psychosocial stress and social withdrawal, and these appear to increase the risk of psychosis relapse. Due to high levels of heterogeneity within the literature, we could only conduct a narrative synthesis of the findings. Future studies would benefit from employing a meta-analytic approach.

Functional brain plasticity following childhood maltreatment: A longitudinal fMRI investigation of autobiographical memory processing.

Altered autobiographical memory (ABM) processing characterizes some individuals with experiences of childhood maltreatment. This fMRI study of ABM processing evaluated potential developmental plasticity in neural functioning following maltreatment. Adolescents with (N = 19; MT group) and without (N = 18; Non-MT group) documented childhood maltreatment recalled specific ABMs in response to emotionally valenced cue words during fMRI at baseline (age 12.71 ± 1.48) and follow-up (14.88 ± 1.53 years). Psychological assessments were collected at both timepoints. Longitudinal analyses were carried out with BOLD signal changes during ABM recall and psychopathology to investigate change over time. In both groups there was relative stability of the ABM brain network, with some developmental maturational changes observed in cortical midline structures (ventromedial PFC (vmPFC), posterior cingulate cortex (pCC), and retrosplenial cortex (rSC). Significantly increased activation of the right rSC was observed only in the MT group, which was associated with improved psychological functioning. Baseline group differences in relation to hippocampal functioning, were not detected at follow-up. This study provides preliminary empirical evidence of functional developmental plasticity in children with documented maltreatment experience using fMRI. This suggests that altered patterns of brain function, associated with maltreatment experience, are not fixed and may reflect the potential to track a neural basis of resilience.

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders.

A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.

Predictors of treatment response in young people at ultra-high risk for psychosis who received long-chain omega-3 fatty acids.

Previous efforts in the prospective evaluation of individuals who experience attenuated psychotic symptoms have attempted to isolate mechanisms underlying the onset of full-threshold psychotic illness. In contrast, there has been little research investigating specific predictors of positive outcomes. In this study, we sought to determine biological and clinical factors associated with treatment response, here indexed by functional improvement in a pre-post examination of a 12-week randomized controlled intervention in individuals at ultra-high risk (UHR) for psychosis. Participants received either long-chain omega-3 (ω-3) polyunsaturated fatty acids (PUFAs) or placebo. To allow the determination of factors specifically relevant to each intervention, and to be able to contrast them, both treatment groups were investigated in parallel. Univariate linear regression analysis indicated that higher levels of erythrocyte membrane α-linolenic acid (ALA; the parent fatty acid of the ω-3 family) and more severe negative symptoms at baseline predicted subsequent functional improvement in the treatment group, whereas less severe positive symptoms and lower functioning at baseline were predictive in the placebo group. A multivariate machine learning analysis, known as Gaussian Process Classification (GPC), confirmed that baseline fatty acids predicted response to treatment in the ω-3 PUFA group with high levels of sensitivity, specificity and accuracy. In addition, GPC revealed that baseline fatty acids were predictive in the placebo group. In conclusion, our investigation indicates that UHR patients with higher levels of ALA may specifically benefit from ω-3 PUFA supplementation. In addition, multivariate machine learning analysis suggests that fatty acids could potentially be used to inform prognostic evaluations and treatment decisions at the level of the individual. Notably, multiple statistical analyses were conducted in a relatively small sample, limiting the conclusions that can be drawn from what we believe to be a first-of-its-kind study. Additional studies with larger samples are therefore needed to evaluate the generalizability of these findings.

Are we really mapping psychosis risk? Neuroanatomical signature of affective disorders in subjects at ultra high risk.

BACKGROUND: The majority of people at ultra high risk (UHR) of psychosis also present with co-morbid affective disorders such as depression or anxiety. The neuroanatomical and clinical impact of UHR co-morbidity is unknown. METHOD: We investigated group differences in grey matter volume using baseline magnetic resonance images from 121 participants in four groups: UHR with depressive or anxiety co-morbidity; UHR alone; major depressive disorder; and healthy controls. The impact of grey matter volume on baseline and longitudinal clinical/functional data was assessed with regression analyses. RESULTS: The UHR-co-morbidity group had lower grey matter volume in the anterior cingulate cortex than the UHR-alone group, with an intermediate effect between controls and patients with major depressive disorder. In the UHR-co-morbidity group, baseline anterior cingulate volume was negatively correlated with baseline suicidality/self-harm and obsessive-compulsive disorder symptoms. CONCLUSIONS: Co-morbid depression and anxiety disorders contributed distinctive grey matter volume reductions of the anterior cingulate cortex in people at UHR of psychosis. These volumetric deficits were correlated with baseline measures of depression and anxiety, suggesting that co-morbid depressive and anxiety diagnoses should be carefully considered in future clinical and imaging studies of the psychosis high-risk state.

Using structural neuroanatomy to identify trauma survivors with and without post-traumatic stress disorder at the individual level.

BACKGROUND: At present there are no objective, biological markers that can be used to reliably identify individuals with post-traumatic stress disorder (PTSD). This study assessed the diagnostic potential of structural magnetic resonance imaging (sMRI) for identifying trauma-exposed individuals with and without PTSD. METHOD: sMRI scans were acquired from 50 survivors of the Sichuan earthquake of 2008 who had developed PTSD, 50 survivors who had not developed PTSD and 40 healthy controls who had not been exposed to the earthquake. Support vector machine (SVM), a multivariate pattern recognition technique, was used to develop an algorithm that distinguished between the three groups at an individual level. The accuracy of the algorithm and its statistical significance were estimated using leave-one-out cross-validation and permutation testing. RESULTS: When survivors with PTSD were compared against healthy controls, both grey and white matter allowed discrimination with an accuracy of 91% (p < 0.001). When survivors without PTSD were compared against healthy controls, the two groups could be discriminated with accuracies of 76% (p < 0.001) and 85% (p < 0.001) based on grey and white matter, respectively. Finally, when survivors with and without PTSD were compared directly, grey matter allowed discrimination with an accuracy of 67% (p < 0.001); in contrast the two groups could not be distinguished based on white matter. CONCLUSIONS: These results reveal patterns of neuroanatomical alterations that could be used to inform the identification of trauma survivors with and without PTSD at the individual level, and provide preliminary support to the development of SVM as a clinically useful diagnostic aid.

Reduced parahippocampal cortical thickness in subjects at ultra-high risk for psychosis.

BACKGROUND: Grey matter volume and cortical thickness represent two complementary aspects of brain structure. Several studies have described reductions in grey matter volume in people at ultra-high risk (UHR) of psychosis; however, little is known about cortical thickness in this group. The aim of the present study was to investigate cortical thickness alterations in UHR subjects and compare individuals who subsequently did and did not develop psychosis. METHOD: We examined magnetic resonance imaging data collected at four different scanning sites. The UHR subjects were followed up for at least 2 years. Subsequent to scanning, 50 UHR subjects developed psychosis and 117 did not. Cortical thickness was examined in regions previously identified as sites of neuroanatomical alterations in UHR subjects, using voxel-based cortical thickness. RESULTS: At baseline UHR subjects, compared with controls, showed reduced cortical thickness in the right parahippocampal gyrus (p < 0.05, familywise error corrected). There were no significant differences in cortical thickness between the UHR subjects who later developed psychosis and those who did not. CONCLUSIONS: These data suggest that UHR symptomatology is characterized by alterations in the thickness of the medial temporal cortex. We did not find evidence that the later progression to psychosis was linked to additional alterations in cortical thickness, although we cannot exclude the possibility that the study lacked sufficient power to detect such differences.

Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level.

BACKGROUND: Group-level results suggest that relative to healthy controls (HCs), ultra-high-risk (UHR) and first-episode psychosis (FEP) subjects show alterations in neuroanatomy, neurofunction and cognition that may be mediated genetically. It is unclear, however, whether these groups can be differentiated at single-subject level, for instance using the machine learning analysis support vector machine (SVM). Here, we used a multimodal approach to examine the ability of structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor neuroimaging (DTI), genetic and cognitive data to differentiate between UHR, FEP and HC subjects at the single-subject level using SVM. METHOD: Three age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEP versus HC, UHR versus HC and FEP versus UHR, respectively. Genetic, sMRI, DTI, fMRI and cognitive data were obtained for each participant and the ability of each to discriminate subjects at the individual level in conjunction with SVM was tested. RESULTS: Successful classification accuracies (p < 0.05) comprised FEP versus HC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHR versus HC (sMRI, 68.42%; DTI, 65.79%), and FEP versus UHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%). CONCLUSIONS: The results suggest that FEP subjects are identifiable at the individual level using a range of biological and cognitive measures. Comparatively, only sMRI and DTI allowed discrimination of UHR from HC subjects. For the first time FEP and UHR subjects have been shown to be directly differentiable at the single-subject level using cognitive, sMRI and fMRI data. Preliminarily, the results support clinical development of SVM to help inform identification of FEP and UHR subjects, though future work is needed to provide enhanced levels of accuracy.

When the single matters more than the group: very high false positive rates in single case Voxel Based Morphometry.

Voxel Based Morphometry (VBM) studies typically involve a comparison between groups of individuals; this approach however does not allow inferences to be made at the level of the individual. In recent years, an increasing number of research groups have attempted to overcome this issue by performing single case studies, which involve the comparison between a single subject and a control group. However, the interpretation of the results is problematic; for instance, any significant difference might be driven by individual variability in neuroanatomy rather than the neuropathology of the disease under investigation, or might represent a false positive due to the data being sampled from non-normally distributed populations. The aim of the present investigation was to empirically estimate the likelihood of detecting significant differences in gray matter volume in individuals free from neurological or psychiatric diagnosis. We compared a total of 200 single subjects against a group of 16 controls matched for age and gender, using two independent datasets from the Neuroimaging Informatics Tools and Resources Clearinghouse. We report that the chance of detecting a significant difference in a disease-free individual is much higher than previously expected; for instance, using a standard voxel-wise threshold of p<0.05 (corrected) and an extent threshold of 10 voxels, the likelihood of a single subject showing at least one significant difference is as high as 93.5% for increases and 71% for decreases. We also report that the chance of detecting significant differences was greatest in frontal and temporal cortices and lowest in subcortical regions. The chance of detecting significant differences was inversely related to the degree of smoothing applied to the data, and was higher for unmodulated than modulated data. These results were replicated in the two independent datasets. By providing an empirical estimation of the number of significant increases and decreases to be expected in each cortical and subcortical region in disease-free individuals, the present investigation could inform the interpretation of future single case VBM studies.

Thalamic glutamate levels as a predictor of cortical response during executive functioning in subjects at high risk for psychosis.

CONTEXT: Alterations in glutamatergic neurotransmission and cerebral cortical dysfunction are thought to be central to the pathophysiology of psychosis, but the relationship between these 2 factors is unclear. OBJECTIVE: To investigate the relationship between brain glutamate levels and cortical response during executive functioning in people at high risk for psychosis (ie, with an at-risk mental state [ARMS]). DESIGN: Subjects were studied using functional magnetic resonance imaging while they performed a verbal fluency task, and proton magnetic resonance spectroscopy was used to measure their brain regional glutamate levels. SETTING: Maudsley Hospital, London, England. PATIENTS AND OTHER PARTICIPANTS: A total of 41 subjects: 24 subjects with an ARMS and 17 healthy volunteers (controls). MAIN OUTCOME MEASURES: Regional brain activation (blood oxygen level-dependent response); levels of glutamate in the anterior cingulate, left thalamus, and left hippocampus; and psychopathology ratings at the time of scanning. RESULTS: During the verbal fluency task, subjects with an ARMS showed greater activation than did controls in the middle frontal gyrus bilaterally. Thalamic glutamate levels were lower in the ARMS group than in control group. Within the ARMS group, thalamic glutamate levels were negatively associated with activation in the right dorsolateral prefrontal and left orbitofrontal cortex, but positively associated with activation in the right hippocampus and in the temporal cortex bilaterally. There was also a significant group difference in the relationship between cortical activation and thalamic glutamate levels, with the control group showing correlations in the opposite direction to those in the ARMS group in the prefrontal cortex and in the right hippocampus and superior temporal gyrus. CONCLUSIONS: Altered prefrontal, hippocampal, and temporal function in people with an ARMS is related to a reduction in thalamic glutamate levels, and this relationship is different from that in healthy controls.

Prefrontal function at presentation directly related to clinical outcome in people at ultrahigh risk of psychosis.

BACKGROUND: The prodromal phase of psychosis is characterized by impaired executive function and altered prefrontal activation. The extent to which the severity of these deficits at presentation predicts subsequent clinical outcomes is unclear. METHODS: We employed functional magnetic resonance imaging in a cohort of subjects at clinical risk for psychosis and in healthy controls. Images were acquired at clinical presentation and again after 1 year, using a 1.5-T Signa MRI scanner while subjects were performing a verbal fluency task. SPM5 was used for the analysis of imaging data. Psychopathological assessment of the "at-risk" symptoms was performed by using the Comprehensive Assessment for the At-Risk Mental State (CAARMS) and the Positive and Negative Symptom Scale (PANSS). RESULTS: In the at-risk mental state (ARMS) group, between presentation and follow-up, the CAARMS (perceptual disorder and thought disorder subscales) and the PANSS general scores decreased, while the Global Assessment of Functioning (GAF) score increased. Both the ARMS and control groups performed the verbal fluency task with a high degree of accuracy. The ARMS group showed greater activation in the left inferior frontal gyrus but less activation in the anterior cingulate gyrus than controls. Within the ARMS group, the longitudinal normalization of neurofunctional response in the left inferior frontal gyrus was positively correlated with the improvement in severity of hallucination-like experiences. CONCLUSIONS: The normalization of the abnormal prefrontal response during executive functioning is associated with 12-month psychopathological improvement of prodromal symptoms.

No association of Disrupted-in-Schizophrenia-1 variation with prefrontal function in patients with schizophrenia and bipolar disorder.

The Disrupted-in-Schizophrenia-1 (DISC1) gene has been implicated in both schizophrenia and bipolar disorder by linkage and genetic association studies. Altered prefrontal cortical function is a pathophysiological feature of both disorders, and we have recently shown that variation in DISC1 modulates prefrontal activation in healthy volunteers. Our goal was to examine the influence of the DISC1 polymorphism Cys704Ser on prefrontal function in schizophrenia and bipolar disorder. From 2004 to 2008, patients with schizophrenia (N = 44), patients with bipolar disorder (N = 35) and healthy volunteers (N = 53) were studied using functional magnetic resonance imaging while performing a verbal fluency task. The effect of Cys704Ser on cortical activation was compared between groups as Cys704 carriers vs. Ser704 homozygotes. In contrast to the significant effect on prefrontal activation we had previously found in healthy subjects, no significant effect of Cys704Ser was detected in this or any other region in either the schizophrenia or bipolar groups. When controls were compared with patients with schizophrenia, there was a diagnosis by genotype interaction in the left middle/superior frontal gyrus [family-wise error (FWE) P = 0.002]. In this region, Ser704/ser704 controls activated more than Cys704 carriers, and there was a trend in the opposite direction in schizophrenia patients. In contrast to its effect in healthy subjects, variation in DISC1 Cys704Ser704 genotype was not associated with altered prefrontal activation in patients with schizophrenia or bipolar disorder. The absence of an effect in patients may reflect interactions of the effects of DISC1 genotype with the effects of other genes associated with these disorders, and/or with the effects of the disorders on brain function.

Schizotypy and brain structure: a voxel-based morphometry study.

BACKGROUND: Schizotypy is conceptualized as a subclinical manifestation of the same underlying biological factors that give rise to schizophrenia and other schizophrenia spectrum disorders. Individuals with psychometric schizotypy (PS) experience subthreshold psychotic signs and can be psychometrically identified among the general population. Previous research using magnetic resonance imaging (MRI) has shown gray-matter volume (GMV) abnormalities in chronic schizophrenia, in subjects with an at-risk mental state (ARMS) and in individuals with schizotypal personality disorder (SPD). However, to date, no studies have investigated the neuroanatomical correlates of PS. METHOD: Six hundred first- and second-year university students completed the Community Assessment of Psychic Experiences (CAPE), a self-report instrument on psychosis proneness measuring attenuated positive psychotic experiences. A total of 38 subjects with high and low PS were identified and subsequently scanned with MRI. Voxel-based morphometry (VBM) was applied to examine GMV differences between subjects with high and low positive PS. RESULTS: Subjects with high positive PS showed larger global volumes compared to subjects with low PS, and larger regional volumes in the medial posterior cingulate cortex (PCC) and the precuneus. There were no regions where GMV was greater in low than in high positive PS subjects. CONCLUSIONS: These regions, the PCC and precuneus, have also been sites of volumetric differences in MRI studies of ARMS subjects and schizophrenia, suggesting that psychotic or psychotic-like experiences may have common neuroanatomical correlates across schizophrenia spectrum disorders.

Dysbindin modulates brain function during visual processing in children.

Schizophrenia is a neurodevelopmental disorder, and risk genes are thought to act through disruption of brain development. Several genetic studies have identified dystrobrevin binding protein 1 (DTNBP1, also known as dysbindin) as a potential susceptibility gene for schizophrenia, but its impact on brain function is poorly understood. It has been proposed that DTNBP1 may be associated with differences in visual processing. To test this, we examined the impact on visual processing in 61 healthy children aged 10-12 years of a genetic variant in DTNBP1 (rs2619538) that was common to all schizophrenia associated haplotypes in an earlier UK-Irish study. We tested the hypothesis that carriers of the risk allele would show altered occipital cortical function relative to noncarriers. Functional Magnetic Resonance Imaging (fMRI) was used to measure brain responses during a visual matching task. The data were analysed using statistical parametric mapping and statistical inferences were made at p<0.05 (corrected for multiple comparisons). Relative to noncarriers, carriers of the risk allele had greater activation in the lingual, fusiform gyrus and inferior occipital gyri. In these regions DTNBP1 genotype accounted for 19%, 20% and 14% of the inter-individual variance, respectively. Our results suggest that that genetic variation in DTNBP1 is associated with differences in the function of brain areas that mediate visual processing, and that these effects are evident in young children. These findings are consistent with the notion that the DTNBP1 gene influences brain development and can thereby modulate vulnerability to schizophrenia.

How reading differs from object naming at the neuronal level.

This paper uses whole brain functional neuroimaging in neurologically normal participants to explore how reading aloud differs from object naming in terms of neuronal implementation. In the first experiment, we directly compared brain activation during reading aloud and object naming. This revealed greater activation for reading in bilateral premotor, left posterior superior temporal and precuneus regions. In a second experiment, we segregated the object-naming system into object recognition and speech production areas by factorially manipulating the presence or absence of objects (pictures of objects or their meaningless scrambled counterparts) with the presence or absence of speech production (vocal vs. finger press responses). This demonstrated that the areas associated with speech production (object naming and repetitively saying "OK" to meaningless scrambled pictures) corresponded exactly to the areas where responses were higher for reading aloud than object naming in Experiment 1. Collectively the results suggest that, relative to object naming, reading increases the demands on shared speech production processes. At a cognitive level, enhanced activation for reading in speech production areas may reflect the multiple and competing phonological codes that are generated from the sublexical parts of written words. At a neuronal level, it may reflect differences in the speed with which different areas are activated and integrate with one another.

Modelling functional integration: a comparison of structural equation and dynamic causal models.

The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.