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Background and Hypothesis: Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks., Study Design: We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom., Study Results: GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis., Conclusions: The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), share clinical and neurobiological features. Because previous investigations of functional dysconnectivity have mainly focused on single disorders, the transdiagnostic alterations in the functional connectome architecture of the brain remain poorly understood. We collected resting-state functional magnetic resonance imaging data from 512 participants, including 121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls. Individual functional brain connectomes were constructed in a voxelwise manner, and the modular architectures were examined at different scales, including (1) global modularity, (2) module-specific segregation and intra- and intermodular connections, and (3) nodal participation coefficients. The correlation of these modular measures with clinical scores was also examined. We reliably identify common alterations in modular organization in patients compared to controls, including (1) lower global modularity; (2) lower modular segregation in the frontoparietal, subcortical, visual, and sensorimotor modules driven by more intermodular connections; and (3) higher participation coefficients in several network connectors (the dorsolateral prefrontal cortex and angular gyrus) and the thalamus. Furthermore, the alterations in the SCZ group are more widespread than those of the BD and MDD groups and involve more intermodular connections, lower modular segregation and higher connector integrity. These alterations in modular organization significantly correlate with clinical scores in patients. This study demonstrates common hyper-integrated modular architectures of functional brain networks among patients with SCZ, BD, and MDD. These findings reveal a transdiagnostic mechanism of network dysfunction across psychiatric disorders from a connectomic perspective., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Background: There are very few close-up sociological or anthropological data informing epidemiological and psychiatric research design and/or contributing to our understanding of the relationship between mental health and specific forms of urban life. Furthermore, research on the relationships between urbanicity and mental disorder has paid little attention to the global diversity of urban experience, such as in cities in China, India and Brazil., Methods: Two innovative methods can be employed to unveil the diversified urban experience of migrants in China, i.e. an ethnography-informed sociological deep surveying instrument and an ecological momentary assessment with a smartphone app. This article introduces the design and pilot survey of these new instruments towards a 'mechanism-rich' epidemiology., Results: The ethnography-informed survey instrument enabled us to include some of the issues from the ethnography and successfully 'dig deeper' into respondents' social experience. The pilot of the smartphone app serves as 'proof of principle' that we can recruit respondents in Shanghai, and that we can receive and use the data., Conclusions: Both of these pilots have demonstrated good feasibility for studying mobility, urban life and mental health. Our next steps will be to extend the Shanghai sample, to use the app in Sao Paulo and Toronto and then hopefully in India and Africa., (© The Author(s) 2019. Published by Oxford University Press Royal Society of Tropical Medicine and Hygiene.)

Objective: Increasingly, studies have identified abnormalities in the functional connectivity (FC) of large-scale neural networks in early psychosis, but the findings thus far have been inconclusive. Therefore, the aim of this study was to identify robust alterations in FC of the default mode (DMN), salience (SN), and central executive networks (CEN), in patients with first-episode psychosis (FEP) using a meta-analytic approach., Methods: Included studies were required to be resting-state, seed-to-whole brain, FC neuroimaging studies, comparing FEP patients to healthy controls (HC), with seeds within the boundaries of the region-of-interest networks. Peak effect coordinates and peak t, z, or p values were meta-analyzed using Seed-based d Mapping software., Results: The DMN seeds primarily displayed within-network hypoconnectivity (largest clusters including the middle orbital gyrus; and ventral anterior cingulate gyrus). The SN seeds displayed hypoconnectivity with regions in the DMN and CEN (largest clusters located in the bilateral middle temporal gyri). Review of the limited CEN data revealed hypo- and hyperconnectivity across the networks. Negative symptoms were positively correlated with all DMN FC abnormalities in the FEP group. Antipsychotic-treated patients displayed greater hypoconnectivity than antipsychotic-naïve patients between both the DMN/SN seeds and prefrontal regions., Conclusions: These findings provide substantial evidence of widespread resting-state FC abnormalities of the DMN, SN, and CEN in early psychosis; particularly implicating DMN and SN dysconnectivity as a core deficit underlying the psychopathology of psychosis. Additionally, we highlight the importance of disentangling connectivity abnormalities resulting from disease processes, from those that result from antipsychotic treatment., (© The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Existing evidence on the beneficial effects of nature on mental health comes from studies using cross-sectional designs. We developed a smartphone-based tool (Urban Mind; www.urbanmind.info ) to examine how exposure to natural features within the built environment affects mental well-being in real time. The tool was used to monitor 108 individuals who completed 3013 assessments over a 1-week period. Significant immediate and lagged associations with mental well-being were found for several natural features. These associations were stronger in people with higher trait impulsivity, a psychological measure of one's tendency to behave with little forethought or consideration of the consequences, which is indicative of a higher risk of developing mental-health issues. Our investigation suggests that the benefits of nature on mental well-being are time-lasting and interact with an individual's vulnerability to mental illness. These findings have potential implications from the perspectives of global mental health as well as urban planning and design.

Background: Functional neuroimaging studies of schizophrenia have identified abnormal activations in many brain regions. In an effort to interpret these findings from a network perspective, we carried out a meta-analysis of this literature, mapping anatomical locations of under- and over-activation to the topology of a normative human functional connectome., Methods: We included 314 task-based functional neuroimaging studies including more than 5000 patients with schizophrenia and over 5000 controls. Coordinates of significant under- or over-activations in patients relative to controls were mapped to nodes of a normative connectome defined by a prior meta-analysis of 1641 functional neuroimaging studies of task-related activation in healthy volunteers., Results: Under-activations and over-activations were reported in a wide diversity of brain regions. Both under- and over-activations were significantly more likely to be located in hub nodes that constitute the "rich club" or core of the normative connectome. In a subset of 121 studies that reported both under- and over-activations in the same patients, we found that, in network terms, these abnormalities were located in close topological proximity to each other. Under-activation in a peripheral node was more frequently associated specifically with over-activation of core nodes than with over-activation of another peripheral node., Conclusions: Although schizophrenia is associated with altered brain functional activation in a wide variety of regions, abnormal responses are concentrated in hubs of the normative connectome. Task-specific under-activation in schizophrenia is accompanied by over-activation of topologically central, less functionally specialized network nodes, which may represent a compensatory response., (© The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Schizophrenia is a disabling clinical syndrome found across the world. While the incidence and clinical expression of this illness are strongly influenced by ethnic factors, it is unclear whether patients from different ethnicities show distinct brain deficits. In this multicentre study, we used structural Magnetic Resonance Imaging to investigate neuroanatomy in 126 patients with first episode schizophrenia who came from 4 ethnically distinct cohorts (White Caucasians, African-Caribbeans, Japanese, and Chinese). Each patient was individually matched with a healthy control of the same ethnicity, gender, and age (±1 year). We report a reduction in the gray matter volume of the right anterior insula in patients relative to controls (P < .05 corrected); this reduction was detected in all 4 ethnic groups despite differences in psychopathology, exposure to antipsychotic medication and image acquisition sequence. This finding provides evidence for a neuroanatomical signature of schizophrenia expressed above and beyond ethnic variations in incidence and clinical expression. In light of the existing literature, implicating the right anterior insula in bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety, we speculate that the neuroanatomical deficit reported here may represent a transdiagnostic feature of Axis I disorders., (© The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Neuroimaging studies of schizophrenia have indicated that the development of auditory verbal hallucinations (AVHs) is associated with altered structural and functional connectivity within the perisylvian language network. However, these studies focussed mainly on either structural or functional alterations in patients with chronic schizophrenia. Therefore, they were unable to examine the relationship between the 2 types of measures and could not establish whether the observed alterations would be expressed in the early stage of the illness. We used diffusion tensor imaging and functional magnetic resonance imaging to examine white matter integrity and functional connectivity within the left perisylvian language network of 46 individuals with an at risk mental state for psychosis or a first episode of the illness, including 28 who had developed AVH group and 18 who had not (nonauditory verbal hallucination [nAVH] group), and 22 healthy controls. Inferences were made at P < .05 (corrected). The nAVH group relative to healthy controls showed a reduction of both white matter integrity and functional connectivity as well as a disruption of the normal structure-function relationship along the fronto-temporal pathway. For all measures, the AVH group showed intermediate values between healthy controls and the nAVH group. These findings seem to suggest that, in the early stage of the disorder, a significant impairment of fronto-temporal connectivity is evident in patients who do not experience AVHs. This is consistent with the hypothesis that, whilst mild disruption of connectivity might still enable the emergence of AVHs, more severe alterations may prevent the occurrence of the hallucinatory experience., (© The Author 2013. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders., (© The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.)

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS., (© The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

It remains unclear whether brain structural abnormalities observed before the onset of psychosis are specific to schizophrenia or are common to all psychotic disorders. This study aimed to measure regional gray matter volume prior to the onset of schizophreniform and of affective psychoses. We investigated 102 subjects at ultrahigh risk (UHR) of developing psychosis recruited from the Personal Assessment and Crisis Evaluation Clinic in Melbourne, Australia. Twenty-eight of these subjects developed psychosis subsequent to scanning: 19 schizophrenia, 7 affective psychoses, and 2 other psychoses. We examined regional gray matter volume using 1.5 mm thick, coronal, 1.5 Tesla magnetic resonance imaging and voxel-based morphometry methods of image analysis. Subjects were scanned at presentation and were followed up clinically for a minimum of 12 months, to detect later transition to psychosis. We found that both groups of subjects who subsequently developed psychosis (schizophrenia and affective psychosis) showed reductions in the frontal cortex relative to UHR subjects who did not develop psychosis. The subgroup that subsequently developed schizophrenia also showed smaller volumes in the parietal cortex and, at trend level, in the temporal cortex, whereas those who developed an affective psychosis had significantly smaller subgenual cingulate volumes. These preliminary findings suggest that volumetric abnormalities in UHR individuals developing schizophrenia vs affective psychoses comprise a combination of features that predate both disorders and others that may be specific to the nature of the subsequent disorder.

Cannabis sativa, the most widely used illicit drug, has profound effects on levels of anxiety in animals and humans. Although recent studies have helped provide a better understanding of the neurofunctional correlates of these effects, indicating the involvement of the amygdala and cingulate cortex, their reciprocal influence is still mostly unknown. In this study dynamic causal modelling (DCM) and Bayesian model selection (BMS) were used to explore the effects of pure compounds of C. sativa [600 mg of cannabidiol (CBD) and 10 mg Delta 9-tetrahydrocannabinol (Delta 9-THC)] on prefrontal-subcortical effective connectivity in 15 healthy subjects who underwent a double-blind randomized, placebo-controlled fMRI paradigm while viewing faces which elicited different levels of anxiety. In the placebo condition, BMS identified a model with driving inputs entering via the anterior cingulate and forward intrinsic connectivity between the amygdala and the anterior cingulate as the best fit. CBD but not Delta 9-THC disrupted forward connectivity between these regions during the neural response to fearful faces. This is the first study to show that the disruption of prefrontal-subocritical connectivity by CBD may represent neurophysiological correlates of its anxiolytic properties.

The relationship between orthography (spelling) and phonology (speech sounds) varies across alphabetic languages. Consequently, learning to read a second alphabetic language, that uses the same letters as the first, increases the phonological associations that can be linked to the same orthographic units. In subjects with English as their first language, previous functional imaging studies have reported increased left ventral prefrontal activation for reading words with spellings that are inconsistent with their orthographic neighbors (e.g., PINT) compared with words that are consistent with their orthographic neighbors (e.g., SHIP). Here, using functional magnetic resonance imaging (fMRI) in 17 Italian-English and 13 English-Italian bilinguals, we demonstrate that left ventral prefrontal activation for first language reading increases with second language vocabulary knowledge. This suggests that learning a second alphabetic language changes the way that words are read in the first alphabetic language. Specifically, first language reading is more reliant on both lexical/semantic and nonlexical processing when new orthographic to phonological mappings are introduced by second language learning. Our observations were in a context that required participants to switch between languages. They motivate future fMRI studies to test whether first language reading is also altered in contexts when the second language is not in use.

Recent neuroimaging studies have reported deficits in functional integration between prefrontal cortex and the hippocampal formation in schizophrenia. It is unclear whether these alterations are a consequence of chronic illness or its treatment, and whether they are also evident in non-psychotic subjects at increased risk of the disorder. We addressed these issues by investigating prefrontal-hippocampal interactions in patients with first episode schizophrenia and subjects with an At Risk Mental State (ARMS). Using functional Magnetic Resonance Imaging, we measured brain responses from 16 individuals with an ARMS, 10 patients with first episode schizophrenia and 14 healthy controls during a delayed matching to sample task. Dynamic causal modelling was used to estimate the effective connectivity between prefrontal cortex and anterior and posterior hippocampal regions. The normal pattern of effective connectivity from the right posterior hippocampus to the right inferior frontal gyrus was significantly decreased in both first episode patients and subjects with an ARMS (ANOVA; F = 8.16, P = 0.01). Interactions between the inferior frontal gyrus and the anterior part of the hippocampus did not differ across the three groups. Perturbed hippocampal-prefrontal interactions are evident in individuals at high risk of developing psychosis and in patients who have just developed schizophrenia. This suggests that it may be a correlate of increased vulnerability to psychosis and that it is not attributable to an effect of chronic illness or its treatment.

Brain imaging studies of adults with psychopathy have identified structural and functional abnormalities in limbic and prefrontal regions that are involved in emotion recognition, decision-making, morality and empathy. Among children with conduct problems, a small subgroup presents callous-unemotional traits thought to be antecedents of psychopathy. No structural brain imaging study has examined this subgroup of children. The present study used voxel-based morphometry to compare whole brain grey matter volumes and concentrations of boys with elevated levels of callous-unemotional conduct problems and typically developing boys and explored four a priori regions of interest. sMRI scans were collected from 23 boys with elevated levels of callous-unemotional conduct problems (mean age = 11 years 8 months) and 25 typically developing boys (mean age = 11 years 6 months) selected from a community sample of children. Data were analysed using optimized voxel-based morphometry. Study-specific probability maps were created and four a priori regions of interest identified (orbitofrontal, anterior cingulate and anterior insular cortices and amygdala). Both grey matter volume and concentration were examined controlling for cognitive ability and hyperactivity-inattention symptoms. Boys with callous-unemotional conduct problems, as compared with typically developing boys, presented increased grey matter concentration in the medial orbitofrontal and anterior cingulate cortices, as well as increased grey matter volume and concentration in the temporal lobes bilaterally. These findings may indicate a delay in cortical maturation in several brain areas implicated in decision making, morality and empathy in boys with callous-unemotional conduct problems.

Dyslexic individuals show subtle impairments in naming pictures of objects in addition to their difficulties with reading. The present study investigated whether word reading and picture naming deficits in developmental dyslexia can be reduced to a common neurological impairment. Eight dyslexic subjects, impaired on measures of reading, spelling and naming speed, were matched for age and general ability with 10 control subjects. Participants were scanned using PET during two experimental conditions: reading words and naming pictures in the form of corresponding line drawings. In addition, two high-level baseline conditions were used to control for visual and articulatory processes. Relative to the control group, the dyslexic participants showed reduced activation in a left occipitotemporal area during both word reading and picture naming. This was the case even in the context of intact behavioural performance during scanning. Abnormal activation in this region, as reported previously for reading, is therefore not specific to orthographic decoding but may reflect a more general impairment in integrating phonology and visual information. Our investigation points to a common neurological basis for deficits in word reading and picture naming in developmental dyslexia.

Functional magnetic resonance imaging (fMRI) studies have identified category-selective regions in ventral occipito-temporal cortex that respond preferentially to faces and other objects. The extent to which these patterns of activation are modulated by bottom-up or top-down mechanisms is currently unknown. We combined fMRI and dynamic causal modelling to investigate neuronal interactions between occipito-temporal, parietal and frontal regions, during visual perception and visual imagery of faces, houses and chairs. Our results indicate that, during visual perception, category-selective patterns of activation in extrastriate cortex are mediated by content-sensitive forward connections from early visual areas. In contrast, during visual imagery, category-selective activation is mediated by content-sensitive backward connections from prefrontal cortex. Additionally, we report content-unrelated connectivity between parietal cortex and the category-selective regions, during both perception and imagery. Thus, our investigation revealed that neuronal interactions between occipito-temporal, parietal and frontal regions are task- and stimulus-dependent. Sensory representations of faces and objects are mediated by bottom-up mechanisms arising in early visual areas and top-down mechanisms arising in prefrontal cortex, during perception and imagery respectively. Additionally non-selective, top-down processes, originating in superior parietal areas, contribute to the generation of mental images, regardless of their content, and their maintenance in the 'mind's eye'.