Identification of a Common Neurobiological Substrate for Mental Illness

By Madeleine S. Goodkind, Simon B. Eickhoff, D. Oathes, Ying Jiang, Andrew Chang, Laura B. Jones-Hagata, Brissa N. Ortega, Yevgeniya V. Zaiko, Erika L. Roach, M. Korgaonkar and 4 more. JAMA psychiatry, 2015

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IMPORTANCE Psychiatric diagnoses are currently distinguished based on sets of specific symptoms. However, genetic and clinical analyses find similarities across a wide variety of diagnoses, suggesting that a common neurobiological substrate may exist across mental illness. OBJECTIVE To conduct a meta-analysis of structural neuroimaging studies across multiple psychiatric diagnoses, followed by parallel analyses of 3 large-scale healthy participant data sets to help interpret structural findings in the meta-analysis. DATA SOURCES PubMed was searched to identify voxel-based morphometry studies through July 2012 comparing psychiatric patients to healthy control individuals for the meta-analysis. The 3 parallel healthy participant data sets included resting-state functional magnetic resonance imaging, a database of activation foci across thousands of neuroimaging experiments, and a data set with structural imaging and cognitive task performance data. DATA EXTRACTION AND SYNTHESIS Studies were included in the meta-analysis if they reported voxel-based morphometry differences between patients with an Axis I diagnosis and control individuals in stereotactic coordinates across the whole brain, did not present predominantly in childhood, and had at least 10 studies contributing to that diagnosis (or across closely related diagnoses). The meta-analysis was conducted on peak voxel coordinates using an activation likelihood estimation approach. MAIN OUTCOMES AND MEASURES We tested for areas of common gray matter volume increase or decrease across Axis I diagnoses, as well as areas differing between diagnoses. Follow-up analyses on other healthy participant data sets tested connectivity related to regions arising from the meta-analysis and the relationship of gray matter volume to cognition. RESULTS Based on the voxel-based morphometry meta-analysis of 193 studies comprising 15 892 individuals across 6 diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety), we found that gray matter loss converged across diagnoses in 3 regions: the dorsal anterior cingulate, right insula, and left insula. By contrast, there were few diagnosis-specific effects, distinguishing only schizophrenia and depression from other diagnoses. In the parallel follow-up analyses of the 3 independent healthy participant data sets, we found that the common gray matter loss regions formed a tightly interconnected network during tasks and at resting and that lower gray matter in this network was associated with poor executive functioning. CONCLUSIONS AND REVELANCE We identified a concordance across psychiatric diagnoses in terms of integrity of an anterior insula/dorsal anterior cingulate-based network, which may relate to executive function deficits observed across diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology, despite likely diverse etiologies, which is currently not an explicit component of psychiatric nosology.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/79b6680d346590801fe9b80522eff061b7bb2667

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Citations in the corpus

The role of prefrontal cortex in cognitive control and executive function (2021, background): “In particular, multiple psychiatric disorders are associated with hypoactivation of the FPN and CON during CC tasks [2, 204], alterations in functional connectivity of these networks at rest [3, 205], and alterations in gray matter volume in nodes of these networks, including the dorsal ACC, insula,”
The Salience Network: A Neural System for Perceiving and Responding to Homeostatic Demands (2019, background): “…example, but schizophrenia, bipolar disorder, major depression, attention-deficit/ hyperactivity, anxiety states, autism spectrum, and substance abuse disorders have now all been linked to volume loss or altered connectivity in salience network regions (Goodkind et al., 2015; Sha et al., 2019).”
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Major depressive disorder (2016, methodology): “Comparisons of MDD with bipolar disorder, schizophrenia, anxiety disorders, obsessive–compulsive disorder or substance use disorder are based on data from Goodkind et al.(91).”
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The Critical Relationship Between Anxiety and Depression. (2020, background): “These findings are consistentwithmeta-analyses that reveal shared structural and functional brain alterations across various psychiatric illnesses, including anxiety andmajor depression, in circuits involving emotion regulation (13), executive function (14), and cognitive control (15).”
Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology (2021, neutral): “Moreover, this principal axis is apparent when examining histology-derived measures of cellular microstructure (Goulas et al., 2018; Paquola et al., 2019a, 2020a), neuron density (Beul and Hilgetag, 2019; Cahalane et al.”
The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core (2016, positive): “…the common neurobiological substrate for mental illness across across six diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety) based on a meta-analysis of grey matter loss in 193 neuroimaging studies of 15892 individuals 51 .”