Predictability of Autism, Schizophrenic and Obsessive Spectra Diagnosis: Toward a Damage Network Approach

Abstract

Schizophrenia, obsessive-compulsive and autistic disorders are traditionally considered as three separate psychiatric conditions each with specific symptoms and pattern of brain alterations. This view can be challenged since these three conditions have the same neurobiological origin, stemming from a common root of a unique neurodevelopmental tree. The aim of this meta-analytic study was to determine, from a neuroimaging perspective, whether i) white matter and gray matter alterations are specific for the three different spectrum disorders, and the nosographical differentiation of three spectra is supported by different patterns of brain alterations. ii) it might be possible to define new spectra starting from specific brain damage. iii) it is possible to detect a “brain damage network” (a connecting link between the damaged areas that relates areas constantly involved in the disorder). Three main findings emerged from our meta-analysis: The three psychiatric spectra do not appear to have their own specific damage. It is possible to define two new damage clusters. The first includes substantial parts of the salience network, and the second is more closely linked to the auditory-visual, auditory and visual somatic areas. It is possible to define a "Damage Network" and to infer a hierarchy of brain substrates in the pattern of propagation of the damage. These results suggest the presence of a common pattern of damage in the three pathologies plus a series of variable alterations that, rather than support the sub-division into three spectra, highlight a two-cluster parcellation with an input-output and more cognitive clusters.

How do morphological alterations caused by chronic pain distribute across the brain? A meta-analytic co-alteration study

“…GM alteration distribution is rather difficult to investigate, mainly because there is a lack of methods specifically devoted to the computation as well as the identification of a GM co - alteration pattern , that is, a type of connectivity that, instead of looking for the transmission of information, looks for the distribution of GM alterations. Recently, however, a study by Cauda et al (2015) has proposed a methodology capable of identifying the neural alterations' distribution across the brain. Authors have shown that, at least for some psychiatric disorders, it is possible to define a morphometric co - alteration network and infer a hierarchy of brain structures within the GM alterations' patterns.…”

“…The present study follows the same line of research and aims at investigating the co-alteration pattern across the brain of patients with chronic pain by applying the method already developed by Cauda et al (2015) . In particular, we used a meta-analytic approach and a network-based analysis in order to address the following issues: i) do neuronal alterations occur randomly across the brain in patients with chronic pain?…”

Role of Ash1l in Tourette syndrome and other neurodevelopmental disorders

“…Cauda et al. (2015) found that the overlapped expression profile between Ash1l and other risk genes, such as SHANK3, DISK1, and NRXN1 , is involved in chromatic regulation and neurodevelopment in early life. Mutations in Ash1l may change early brain developmental events, which is associated with neuropathological abnormalities such as migration, stratification, and proliferative defects.…”

“…Meta‐analysis studies identified 109 loci with pleiotropic effects, including Ash1l , associated with TS and co‐occurrence with psychiatric disorders (Brinkmeier et al., 2015; Lee et al., 2019). These conditions have the same neurobiological origin, originating from the common root of a unique neurodevelopmental tree (Cauda et al., 2015), and the shared etiology and susceptibility show considerable overlap in the common network of brain connectomes, neuroanatomically, in functional or structural aspects (Crossley et al, 2014; van den Heuvel & Sporns, 2019), indicating that an individual mutation and molecular mechanism is unlikely to underlie these diseases (Harrison & Weinberger, 2005; Toro et al., 2010). Convergent evidence shows that connectivity abnormalities, neuronal circuit problems, brain networks, and E/I imbalance are core parts of the pathology in TS, ASD, ADHD, and SCZ.…”

“…Clinically, tic usually onsets at 4–6 years of age, exhibiting a waxing and waning course, and typically improving in adolescence (Browne et al., 2015). Remarkably, TS patients are commonly associated with co‐occurring comorbidities and overlapping behavioral and emotional conditions, including obsessive–compulsive disorder, attention deficit/hyperactivity disorder (ADHD), other possible autism spectrum disorder (ASD) symptoms, and co‐existing mental illnesses (depression, anxiety, antagonistic defiant disease, conduct disorder, and/or personality disorder) (Freeman et al., 2000; Robertson, 2006, 2015; Robertson et al., 2015; Clarke et al., 2012; Tsetsos et al., 2016), highlighting the overlapping circuitry alterations and the existence of connectional co‐substrates in brain networks that are potentially linked in TS and other neuropsychiatric disorders (Cauda et al., 2015; Church et al., 2009; Cravedi et al., 2017; Cross‐Disorder Group of the Psychiatric Genomics Consortium, et al, 2013; Harrison & Weinberger, 2005; Hirschtritt et al., 2015; Huisman‐van Dijk et al., 2016; Cross‐Disorder Group of the Psychiatric Genomics Consortium et al, 2013; Lee et al, 2019; Mathews & Grados, 2011; Robertson et al., 2017; Robertson et al., 2015; van den Heuvel & Sporns, 2019; Vissers et al., 2012). Neurobiologically, convergent results strongly support brain connectivity abnormalities and dysfunction of cortico‐striato‐thalamo‐cortical (CSTC) networks and dopaminergic systems in individuals with TS (Church et al., 2009), which play a critical role in movement control and output and are consistent with imaging evidence.…”