Psychiatry - Research Publications
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ItemRich club and reward network connectivity as endophenotypes for alcohol dependence: a diffusion tensor imaging study(WILEY, 2019-03)We aimed to examine the whole-brain white matter connectivity and local topology of reward system nodes in patients with alcohol use disorder (AUD) and unaffected siblings, relative to healthy comparison individuals. Diffusion-weighted magnetic resonance imaging scans were acquired from 18 patients with AUD, 15 unaffected siblings of AUD patients and 15 healthy controls. Structural networks were examined using network-based statistic and connectomic analysis. Connectomic analysis showed a significant ordered difference in normalized rich club organization (AUD < Siblings < Controls). We also found rank ordered differences (Control > Sibling > AUD) for both nodal clustering coefficient and nodal local efficiency in reward system nodes, particularly left caudate, right putamen and left hippocampus. Network-based statistic analyses showed that AUD group had significantly weaker connectivity than controls in the right hemisphere, mostly in the edges connecting putamen and hippocampus with other brain regions. Our results suggest that reward system network abnormalities, especially in subcortical structures, and impairments in rich-club organization might be related to the familial predisposition for AUD.
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ItemA cautionary note on the use of SIFT in pathological connectomes(WILEY, 2020-03)
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ItemMapping connectomes with diffusion MRI: deterministic or probabilistic tractography?(WILEY, 2019-02)PURPOSE: Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. METHODS: This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. RESULTS: For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42). CONCLUSIONS: Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.
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ItemO2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS(Oxford University Press (OUP), 2020-05-18)Abstract Background Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychopathology. Distinct from chronological age, brain age refers to the age of an individual that is inferred from a normative model of brain structure for individuals of the same age and sex. We predicted brain age from a common set of grey matter features and examined whether the difference between an individual’s chronological and brain age was associated with the severity of psychopathology in children and adolescents. Methods Participants included 1313 youths (49.8% male) aged 8–21 who underwent structural imaging as part of the Philadelphia Neurodevelopmental Cohort. Independent Component Analysis was used to obtain 7 psychopathology dimensions representing Conduct, Anxiety, Obsessive-Compulsive, Attention, Depression, Bipolar, and Psychosis symptoms and an overall measure of severity (General Psychopathology). Using 10-fold cross-validation, support vector machine regression was trained in 402 typically developing youth to predict individual age based on a feature space comprising 111 grey matter regions. This yielded a brain age prediction for each individual. Brain age gap was calculated for each individual by subtracting chronological age from predicted brain age. The general linear model was used to test for an association between brain age gap and each of the 8 dimensions of psychopathology in a test sample of 911 youth. The regional specificity and spatial pattern of brain age gap was also investigated. Error control across the 8 models was achieved with a false discovery rate of 5%. Results Brain age gap was significantly associated with dimensions characterizing obsessive-compulsive (t=2.5, p=0.01), psychosis (t=3.16, p=0.0016) and general psychopathology (t=4.08, p<0.0001). For all three dimensions, brain age gap was positively associated with symptom severity, indicating that individuals with a brain that was predicted to be ‘older’ than expectations set by youth of the same chronological age and sex tended to have higher symptom scores. Findings were confirmed with a categorical approach, whereby higher brain age gap was observed in youth with a lifetime endorsement of psychosis (t=2.35, p=0.02) and obsessive-compulsive (t=2.35, p=0.021) symptoms, in comparison to typically developing individuals. Supplementary analyses revealed that frontal grey matter was the most important feature mediating the association between brain age gap and psychosis symptoms, whereas subcortical volumes were most important for the association between brain age gap and obsessive-compulsive and general symptoms. Discussion We found that the brain was ‘older’ in youth experiencing higher subclinical symptoms of psychosis, obsession-compulsion, and general psychopathology, compared to normally developing youth of the same chronological age. Our results suggest that deviations in normative brain age patterns in youth may contribute to the manifestation of specific psychiatric symptoms of subclinical severity that cut across psychopathology dimensions.
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ItemS187. EXPLORING NEURODEVELOPMENTAL AND FAMILIAL ORIGINS OF NEUROLOGICAL SOFT SIGNS IN SCHIZOPHRENIA(Oxford University Press (OUP), 2020-05-18)Abstract Background The neurodevelopmental hypothesis is the most widely regarded framework for understanding the development of schizophrenia. One of the most commonly cited pieces of evidence for this theory is the presence of neurological soft signs (NSS) in individuals prior to the onset of psychosis. Increased NSS is also reported in unaffected individuals with a family history of schizophrenia, suggesting that NSS may also have a familial component. Although much research has implicated reduced grey matter volume (GMV) in association with these signs, a subcomponent of volume, known as gyrification, has been poorly researched. Given that gyrification develops predominantly in prenatal life it may be particularly susceptible to a neurodevelopmental abnormality. The aims of this study were to investigate the neurodevelopmental and familial underpinnings of NSS in schizophrenia. Specifically, we examined the brain structural correlates, at both the level of GMV and gyrification, of NSS in individuals with schizophrenia, their unaffected relatives and healthy controls. We aimed to determine whether gyrification better predicted NSS severity than GMV, and whether the relationship between brain structure and NSS were present in a step-wise manner across the diagnostic groups. Methods The sample consisted of individuals with schizophrenia (N=66), their unaffected relatives (N=27) and healthy controls (N=53). NSS was assessed with the Neurological Evaluation Scale (NES), and GMV and gyrification were extracted from MRI using the FreeSurfer imaging suite. A series of analysis of covariance were used to compare NES scores and brain measures between the groups. Separate linear regression analyses were used to assess whether whole-brain GMV and gyrification predicted NES above a covariate-only model. Moderation analyses were used to assess whether the relationship between NES and brain structure were different between the diagnostic groups. Error control was achieved with a false discovery rate of 5%. Results NES was significantly higher in schizophrenia patients than relatives (p<.0001), who were in turn significantly higher than controls (p=.034). With the groups combined, lower GMV (p<.0001), as well as lower gyrification (p=.004), predicted higher NES above a covariate-only model. GMV predicted greater variance in NSS in comparison to gyrification, explaining an additional 20.3% of the variance in NES, in comparison to the additional 5.5% of variance in NES explained by gyrification. Diagnostic group moderated the association between GMV and NES (p=.019), but not between gyrification and NES (p=.245). Follow-up tests revealed that lower GMV was associated with higher NES in schizophrenia (t=-4.5, p<.0001) and relatives (t=-2.5, p=.015) but not controls (t=-1.9, p=.055). Discussion Our findings indicate that NSS is heritable, being present in patients with established schizophrenia, and to a lesser extent, in unaffected relatives. Consistent with previous research, we revealed that GMV predicted NSS severity, suggesting that abnormalities in volume may underlie these signs. We additionally found that gyrification predicted, although to a lesser extent than volume, NSS severity, providing some support for schizophrenia being of possible neurodevelopmental origin. Evidence for an association between volume and NSS in relatives, whom are not confounded by illness-related factors such as medication and symptom severity, indicates a familial contribution to the neural underpinnings of NSS. Together, our study suggests that there may be various aetiological pathways underlying soft signs across the schizophrenia diathesis, some that may be of familial or neurodevelopmental origin.
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ItemAberrant Metabolic Patterns Networks in Insular Epilepsy(FRONTIERS MEDIA SA, 2020-12-23)Introduction: Insular epilepsy is clinically challenging. This study aimed to map cerebral metabolic networks in insular epilepsy and investigate their graph-theoretic properties, with the goal of elucidating altered metabolic network architectures that underlie interictal hypometabolism. Aims: Fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) imaging was performed in 17 individuals with a stereoelectroencephalography (SEEG) confirmed diagnosis of insula epilepsy and 14 age- and sex-matched healthy comparison individuals. Metabolic covariance networks were mapped for each group and graph theoretical analyses of these networks were undertaken. For each pair of regions comprising a whole-brain parcellation, regionally-averaged FDG uptake values were correlated across individuals to estimate connection weights. Results: Correlation in regionally-averaged FDG uptake values in the insular epilepsy group was substantially increased for several pairs of regions compared to the healthy comparison group, particularly for the opercular cortex and subcortical structures. This effect was less prominent in brainstem structures. Metabolic covariance networks in the epilepsy group showed reduced small-worldness as well as altered nodal properties in the ipsilateral hemisphere, compared to the healthy comparison group. Conclusions: Cerebral glucose metabolism in insular epilepsy is marked by a lack of normal regional heterogeneity in metabolic patterns, resulting in metabolic covariance networks that are more tightly coupled between regions than healthy comparison individuals. Metabolic networks in insular epilepsy exhibit altered topological properties and evidence of potentially compensatory formation of aberrant local connections. Taken together, these results demonstrate that insular epilepsy is a systemic neurological disorder with widespread disruption to cerebral metabolic networks.
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ItemEstimating the impact of structural directionality: How reliable are undirected connectomes?(MIT PRESS, 2018)Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.
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ItemAdvanced Diffusion Imaging in Psychosis Risk: a cross-sectional and longitudinal study of white matter development(Oxford University Press, 2020-04-01)Background: Studies in individuals at clinical high risk (CHR) for psychosis provide a powerful means to predict outcomes and inform putative mechanisms underlying conversion to psychosis. In previous work, we applied advanced diffusion imaging methods to reveal that white matter pathology in a CHR population is characterized by cellular-specific changes in white matter, suggesting a preexisting neurodevelopmental anomaly. However, it remains unknown whether these deficits relate to clinical symptoms and/or conversion to frank psychosis. To address this gap, we examined cross-sectional and longitudinal white matter maturation in the largest imaging population of CHR individuals to date, obtained from the North American Prodrome Longitudinal Study (NAPLS-3). Methods: Multi-shell diffusion magnetic resonance imaging (MRI) data were collected across multiple timepoints (1–6 at ~2 month intervals) in 286 subjects (age range=12–32 years). These were 230 unmedicated CHR subjects, including 11% (n=25) who transitioned to psychosis (CHR-converters), as well as 56 age and sex-matched healthy controls. Raw diffusion signals were harmonized to remove scanner/site-induced effects, yielding a unified imaging dataset. Fractional anisotropy of cellular tissue (FAt) and the volume fraction of extracellular free-water (FW) were assessed in 12 major tracts from the IIT Human Brain Atlas (v.5.0). Linear mixed effects (LME) models were fitted to infer developmental trajectories of FAt and FW across age for CHR-converters, CHR-nonconverters and control groups, while accounting for the repeated measurements on each individual. Results: The rate at which FAt changed with age significantly differed between the three groups across commissural and association tracts (5 in total; p<0.05). In these tracts, FAt increased with age in controls (0.002% change per year) and in CHR-nonconverters, albeit at a slower rate (0.00074% per year). In contrast, FAt declined with age in CHR-converters at a rate that was significantly faster (-3.944% per year) than the rate of increase in the other two groups. By 25 years of age, FAt was significantly lower in both CHR groups compared to controls (p<0.05). With regard to FW, the rate of change significantly differed between CHR-converters and controls across the forceps major and the left inferior longitudinal and fronto‐occipital fasciculi (IFOF; 3 tracts in total; p<0.05). This was due to increased FW with age in the CHR-converters (0.0024% change per year) relative to controls (-0.0002% per year). Consequently, FW was significantly higher in CHR-converters compared to controls by 20 years of age (p<.05). With regard to symptoms, there was a significant impact of IFOF FW on positive symptom severity across CHR subjects, regardless of conversion status (t=2.37, p<0.05). Discussion: Our results revealed that clinical high-risk for psychosis is associated with cellular-specific alterations in white matter, regardless of conversion status. Only converters showed excess extracellular free-water, which involved tracts connecting occipital, posterior temporal, and orbito-frontal areas. We also demonstrate a direct impact of free-water on positive symptomatology, collectively, suggesting that excess free-water may signal acute psychosis and its onset. This marker may be useful for patient selection for clinical trials and assessment of individuals with prodromal psychosis.
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ItemBuilding connectomes using diffusion MRI: why, how and but(WILEY, 2019-04)Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.