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Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
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2
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Gray JP, Manuello J, Alexander-Bloch AF, Leonardo C, Franklin C, Choi KS, Cauda F, Costa T, Blangero J, Glahn DC, Mayberg HS, Fox PT. Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects. Neuroinformatics 2022; 21:443-455. [PMID: 36469193 DOI: 10.1007/s12021-022-09614-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2022] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.
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Wu R, Gao Y, Zhang H, Chen Y, Tan F, Zeng D, Wan H, Yang Y, Gu J, Pei Z. Metabolic assessment of cerebral palsy with normal clinical MRI using 18F-FDG PET imaging: A preliminary report. Front Neurol 2022; 13:844911. [PMID: 36188357 PMCID: PMC9520285 DOI: 10.3389/fneur.2022.844911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
To explore the cerebral metabolic patterns of cerebral palsy (CP) patients without structural abnormalities by brain magnetic resonance imaging (MRI) scans, we evaluated 18F-fluoro-deoxyglucose positron emission tomography (18F-FDG PET) imaging features in patients. Thirty-one children with CP [Gross Motor Function Classification System (GMFCS) levels II-V] showing no structural abnormalities by MRI were enrolled in this study. Regional glucose metabolic activity values were calculated using Scenium software and compared between the right and left cerebral hemispheres. These comparisons revealed asymmetric metabolic reductions in the central region, cerebellum, frontal lobe, and parietal lobe (p < 0.01). We next determined whether averaged brain metabolic activity values in different brain regions correlated with GMFCS levels. The metabolic activity values of basal ganglia, left temporal lobe, and cerebellum correlated negatively with GMFCS scores (all p < 0.05). This method was applied to the left cerebellum, which showed higher metabolic activity values than those in the right cerebellum in most patients (83.8%), and these values also correlated negatively with GMFCS scores (Spearman's r = −0.36, p = 0.01). Differential cortical glucose metabolism by 18F-FDG PET, may help to distinguish between different CP diagnoses that are not detected by MRI.
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Affiliation(s)
- Ruimin Wu
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yan Gao
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Huaqiong Zhang
- Department of Nursing, Hubei University of Medicine, Shiyan, China
| | - Yijia Chen
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Fan Tan
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Daobing Zeng
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Huabing Wan
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yi Yang
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Jiaowei Gu
- Department of Pediatrics, Hubei University of Medicine, Shiyan, China
- Jiaowei Gu
| | - Zhijun Pei
- Department of Nuclear Medicine and Institute of Anesthesiology and Pain, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Hubei Key Laboratory of Embryonic Stem Cell Research, Shiyan, China
- *Correspondence: Zhijun Pei
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4
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Manuello J, Mancuso L, Liloia D, Cauda F, Duca S, Costa T. A co-alteration parceling of the cingulate cortex. Brain Struct Funct 2022; 227:1803-1816. [PMID: 35238998 PMCID: PMC9098570 DOI: 10.1007/s00429-022-02473-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/14/2022] [Indexed: 11/24/2022]
Abstract
The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case-control voxel-based morphometry experiments were analyzed, and the Patel's κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.
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Affiliation(s)
- Jordi Manuello
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy. .,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.,Neuroscience Institute of Turin, Turin, Italy
| | - Sergio Duca
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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5
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Interhemispheric co-alteration of brain homotopic regions. Brain Struct Funct 2021; 226:2181-2204. [PMID: 34170391 PMCID: PMC8354999 DOI: 10.1007/s00429-021-02318-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/07/2021] [Indexed: 11/11/2022]
Abstract
Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.
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Costa T, Manuello J, Ferraro M, Liloia D, Nani A, Fox PT, Lancaster J, Cauda F. BACON: A tool for reverse inference in brain activation and alteration. Hum Brain Mapp 2021; 42:3343-3351. [PMID: 33991154 PMCID: PMC8249901 DOI: 10.1002/hbm.25452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 02/03/2021] [Accepted: 04/10/2021] [Indexed: 01/17/2023] Open
Abstract
Over the past decades, powerful MRI‐based methods have been developed, which yield both voxel‐based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived‐maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.
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Affiliation(s)
- Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Mario Ferraro
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy.,Department of Physics, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Jack Lancaster
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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7
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Vanasse TJ, Fox PT, Fox PM, Cauda F, Costa T, Smith SM, Eickhoff SB, Lancaster JL. Brain pathology recapitulates physiology: A network meta-analysis. Commun Biol 2021; 4:301. [PMID: 33686216 PMCID: PMC7940476 DOI: 10.1038/s42003-021-01832-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 02/11/2021] [Indexed: 01/31/2023] Open
Abstract
Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic 'cost' significantly differs along this transdiagnostic/multimodal gradient.
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Affiliation(s)
- Thomas J Vanasse
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- South Texas Veterans Health Care System, San Antonio, TX, USA.
| | - P Mickle Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Franco Cauda
- FocusLab and GCS-fMRI, University of Turin and Koelliker Hospital, Turin, Italy
| | - Tommaso Costa
- FocusLab and GCS-fMRI, University of Turin and Koelliker Hospital, Turin, Italy
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, UK
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Jack L Lancaster
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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8
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Liloia D, Mancuso L, Uddin LQ, Costa T, Nani A, Keller R, Manuello J, Duca S, Cauda F. Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence. Neuroimage Clin 2021; 30:102583. [PMID: 33618237 PMCID: PMC7903137 DOI: 10.1016/j.nicl.2021.102583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/15/2020] [Accepted: 01/30/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored. METHODS An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD. RESULTS Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations. CONCLUSION These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy.
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
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9
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Liloia D, Brasso C, Cauda F, Mancuso L, Nani A, Manuello J, Costa T, Duca S, Rocca P. Updating and characterizing neuroanatomical markers in high-risk subjects, recently diagnosed and chronic patients with schizophrenia: A revised coordinate-based meta-analysis. Neurosci Biobehav Rev 2021; 123:83-103. [PMID: 33497790 DOI: 10.1016/j.neubiorev.2021.01.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 01/07/2021] [Accepted: 01/15/2021] [Indexed: 01/10/2023]
Abstract
Characterizing neuroanatomical markers of different stages of schizophrenia (SZ) to assess pathophysiological models of how the disorder develops is an important target for the clinical practice. We performed a meta-analysis of voxel-based morphometry studies of genetic and clinical high-risk subjects (g-/c-HR), recently diagnosed (RDSZ) and chronic SZ patients (ChSZ). We quantified gray matter (GM) changes associated with these four conditions and compared them with contrast and conjunctional data. We performed the behavioral analysis and networks decomposition of alterations to obtain their functional characterization. Results reveal a cortical-subcortical, left-to-right homotopic progression of GM loss. The right anterior cingulate is the only altered region found altered among c-HR, RDSZ and ChSZ. Contrast analyses show left-lateralized insular, amygdalar and parahippocampal GM reduction in RDSZ, which appears bilateral in ChSZ. Functional decomposition shows involvement of the salience network, with an enlargement of the sensorimotor network in RDSZ and the thalamus-basal nuclei network in ChSZ. These findings support the current neuroprogressive models of SZ and integrate this deterioration with the clinical evolution of the disease.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Claudio Brasso
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), University of Turin, Turin, Italy.
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Paola Rocca
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), University of Turin, Turin, Italy.
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10
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Nani A, Manuello J, Mancuso L, Liloia D, Costa T, Vercelli A, Duca S, Cauda F. The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting. Neuroimage 2020; 225:117481. [PMID: 33122115 DOI: 10.1016/j.neuroimage.2020.117481] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/14/2022] Open
Abstract
Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.
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Affiliation(s)
- Andrea Nani
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Jordi Manuello
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Donato Liloia
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Tommaso Costa
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy.
| | - Alessandro Vercelli
- Neuroscience Institute of Turin, Turin, Italy; Neuroscience Institute Cavalieri Ottolenghi, Turin, Italy; Department of Neuroscience, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy; Neuroscience Institute of Turin, Turin, Italy
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Cauda F, Nani A, Liloia D, Manuello J, Premi E, Duca S, Fox PT, Costa T. Finding specificity in structural brain alterations through Bayesian reverse inference. Hum Brain Mapp 2020; 41:4155-4172. [PMID: 32829507 PMCID: PMC7502845 DOI: 10.1002/hbm.25105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/19/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.
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Affiliation(s)
- Franco Cauda
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Donato Liloia
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Jordi Manuello
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali CiviliSpedali Civili HospitalBresciaItaly
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Sergio Duca
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Peter T. Fox
- Research Imaging InstituteUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Tommaso Costa
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
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Mancuso L, Fornito A, Costa T, Ficco L, Liloia D, Manuello J, Duca S, Cauda F. A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders. Neuroimage 2020; 222:117220. [PMID: 32777357 DOI: 10.1016/j.neuroimage.2020.117220] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/24/2020] [Accepted: 07/28/2020] [Indexed: 12/14/2022] Open
Abstract
Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.
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Affiliation(s)
- Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University,Victoria, Australia; Monash Biomedical Imaging, Monash University,Victoria, Australia
| | - Tommaso Costa
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
| | - Linda Ficco
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
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Cauda F, Mancuso L, Nani A, Costa T. Heterogeneous neuroimaging findings, damage propagation and connectivity: an integrative view. Brain 2019; 142:e17. [DOI: 10.1093/brain/awz080] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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Liloia D, Cauda F, Nani A, Manuello J, Duca S, Fox PT, Costa T. Low entropy maps as patterns of the pathological alteration specificity of brain regions: A meta-analysis dataset. Data Brief 2018; 21:1483-1495. [PMID: 30510978 PMCID: PMC6258253 DOI: 10.1016/j.dib.2018.10.142] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 10/25/2018] [Accepted: 10/26/2018] [Indexed: 01/09/2023] Open
Abstract
The data presented in this article are related to the research article entitled "The alteration landscape of the cerebral cortex" (Cauda et al., 2018). Here, we applied a metric called alteration negentropy (A-negentropy) on a large human neuroimaging dataset, in order to denote the "low structural alteration variety" of the altered brain areas. Furthermore, we reported the overview of the selection strategy, as well as the description and distribution of the selected studies from the voxel-based morphometry database of BrainMap (Vanasse et al., 2018). For all of the analyzed brain areas, we reported the number of pathologies affecting them (both local maxima and mean value), as well as the peak and average values of A-negentropy. Regions altered by a small number of brain disorders exhibit high values of A-negentropy.
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Affiliation(s)
- Donato Liloia
- GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
- Corresponding author at: Department of Psychology, University of Turin, Turin, Italy.
| | - Andrea Nani
- GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, USA
- South Texas Veterans Health Care System, San Antonio, TX, USA
| | - Tommaso Costa
- GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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