A meta-analytic approach to disentangle gray matter volume and concentration in pathological brain
Poster No:
1984
Submission Type:
Abstract Submission
Authors:
Donato Liloia1, Annachiara Crocetta1, Denisa Zamfira2, Masaru Tanaka3, Jordi Manuello1, Roberto Keller4, Mauro Cozzolino5, Sergio Duca6, Franco Cauda1, Tommaso Costa1
Institutions:
1FocusLab, Department of Psychology, University of Turin, Italy, Torino, Torino, 2Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy, Milano, Milano, 3HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged, Szeged, Hungary, 4Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy, Torino, Torino, 5Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Salerno, 6GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy, Torino, Torino
First Author:
Co-Author(s):
Denisa Zamfira
Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
Milano, Milano
Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
Milano, Milano
Masaru Tanaka
HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged
Szeged, Hungary
HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged
Szeged, Hungary
Mauro Cozzolino
Department of Humanities, Philosophical and Educational Sciences, University of Salerno
Fisciano, Salerno
Department of Humanities, Philosophical and Educational Sciences, University of Salerno
Fisciano, Salerno
Sergio Duca
GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
Torino, Torino
GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
Torino, Torino
Introduction:
Voxel Based Morphometry (VBM) is one of the most widely used computational tools to gain insights into the neuroanatomical underpinnings of the pathological brain[1]. However, ongoing debates revolve around the analysis choices that have to be made to implement this technique[2][3], particularly concerning those that can lead to generate measurements related to variations in gray matter volumes (GMV) or gray matter concentrations (GMC) [4][5][6]. In our study, we conducted a meta-analytic investigation to systematically examine potential overlaps or distinctions between these two measurements, taking Autism Spectrum Disorder (ASD) as the case of study.
Methods:
Our systematic search identified 69 VBM experiments on subjects with ASD, aiming to identify consistent patterns of GMV and/or GMC variations. The primary GMV dataset included 58 experiments, for a total of 1810 subjects with ASD compared with 2003 typically developing controls (TDCs). The GMC dataset included instead 11 experiments, for a total of 210 subjects with ASD compared with 222 TDCs.
Anisotropic effect-size Signed Differential Mapping (AES-SDM) [7], a coordinate-based meta-analysis (CBMA) method, was employed. Data were organized into primary datasets (GMV and GMC) by grouping VBM-modulated and VBM-non modulated experimental findings, and into sub-groupings related to the age of the samples (pediatric and adult). AES-SDM facilitated a voxel-wise analysis, allowing the comparison of GMV and GMC variations, and reliability testing was performed [8]. Additionally, a psychological and functional association analysis using NeuroSynth [9] and large-scale functional network decomposition were conducted to provide a comprehensive understanding of the neurobiological substrate of ASD.
Anisotropic effect-size Signed Differential Mapping (AES-SDM) [7], a coordinate-based meta-analysis (CBMA) method, was employed. Data were organized into primary datasets (GMV and GMC) by grouping VBM-modulated and VBM-non modulated experimental findings, and into sub-groupings related to the age of the samples (pediatric and adult). AES-SDM facilitated a voxel-wise analysis, allowing the comparison of GMV and GMC variations, and reliability testing was performed [8]. Additionally, a psychological and functional association analysis using NeuroSynth [9] and large-scale functional network decomposition were conducted to provide a comprehensive understanding of the neurobiological substrate of ASD.
Results:
Distinct patterns of GMV and GMC variations were observed in individuals with ASD, encompassing both increased and decreased clusters compared to TDCs. Age-stratified analyses indicated dynamic variations across the lifespan, with unique patterns in pediatric and adult groups. Notable GMV changes included a decrease in the right crus I of the cerebellum and increases in various regions in the left hemisphere (Figure 1). GMC analyses highlighted areas associated with sensorimotor and executive control functions, such as the Anterior Cingulate Cortex (ACC) and Superior Temporal Gyrus (STG) (Figure 2).
Psychological association analyses revealed that GMV alterations were linked to learning, memory, and social functions, while GMC alterations were associated with perception, action, executive control, and emotion. Network decomposition analysis indicated the involvement of the Default Mode Network with GMV and frontoparietal and sensorimotor networks with GMC. Contrast analyses between GMV and GMC datasets unveiled non-overlapping patterns, challenging the assumption of their equivalence.
Psychological association analyses revealed that GMV alterations were linked to learning, memory, and social functions, while GMC alterations were associated with perception, action, executive control, and emotion. Network decomposition analysis indicated the involvement of the Default Mode Network with GMV and frontoparietal and sensorimotor networks with GMC. Contrast analyses between GMV and GMC datasets unveiled non-overlapping patterns, challenging the assumption of their equivalence.
Conclusions:
The study's findings provide clear insights into distinct GMV and GMC patterns, cautioning against assuming their equivalence. Dynamic variations across age groups underscore the complexity of ASD pathology. Associations with cognitive functions and involvement of specific brain networks contribute valuable insights into the neuroanatomical and functional aspects of ASD. The study advocates for ongoing research to unravel the complexities of gray matter alterations, aiming to enhance diagnostic accuracy and develop targeted therapeutic interventions for ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Other Methods 1
Keywords:
Autism
Cerebellum
Meta- Analysis
1|2Indicates the priority used for review
Provide references using author date format
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