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’.
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.
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.
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%).
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.
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.
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.
This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging ...(fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both concerning the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.
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 (
= 19; MT group) and without (
= 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.
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.
► Single subject VBM shows high false positive rates in the healthy population. ► Differences in single subjects are more likely to reflect increases than decreases. ► Differences are mainly located in frontal and temporal areas of the neocortex. ► Single subject VBM studies of patients should be interpreted with caution.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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