People with psychosis have weaker connectivity in the upper temporal area


Just as a small airport would have challenges handling massive plane traffic, people with psychosis may experience communication difficulties because non-language parts of the brain are trying to manage communications tasks, new research from Western and Lawson Health Research Institute shows.

Difficulties with communication – the ability to use language and to comprehend what others are saying – are among the earliest symptoms of psychosis, a mental illness characterized by changes in emotions, impaired functioning and a disconnection from reality.

This could be happening because parts of the brain not meant to process language are trying to perform this complex job, the new research shows.

“The language system seems to be key to understanding this illness,” said Dr. Lena Palaniyappan, professor and Tanna Schulich Chair in Neuroscience and Mental Health at Western’s Schulich School of Medicine & Dentistry and scientist at Lawson and Robarts Research Institute.

“We don’t yet fully understand how the disorganization of language takes place in patients affected by psychosis.”

Embarking on a mission to find out, Dr. Palaniyappan worked with a team of imaging scientists at Robarts to perform magnetic resonance imaging (MRI) scans on the brains of patients with acute psychosis. Patients were recruited from the Prevention and Early Intervention Program for Psychoses (PEPP) at London Health Sciences Centre, a flagship clinic that supports young individuals from an early stage of psychosis.

The team divided the patients into two groups: those with severe language disturbances, and those whose language symptoms were less pronounced. They found that both groups had weakened connectivity, or ‘hubness,’ in the superior temporal area, the part of the brain generally associated with language.

The group with more severe language symptoms also greater connectivity in unexpected regions of the brain that seemed to be compensating for some of the loss of connectivity elsewhere.

Like a small airport trying to handle all the air traffic from a big hub like Pearson International Airport, some non-language brain regions may be overloaded in psychosis, the study says.

“This finding led us to believe that the language problems may occur because the main hubs that are supposed to conduct language are now retired, and so these peripheral hubs – which have no business of orchestrating language as their main function – are picking up the job and aren’t doing it very well,” said Palaniyappan.

The researchers hope that this new understanding of how language becomes disorganized in psychosis can inform new interventions to focus on strengthening the language systems in the brain to reduce or delay psychotic symptoms.

Using ultra-high-field magnetic resonance imaging at Robarts, they were able to look at the entire brain of patients with acute psychosis. Instead of homing in on one specific area, the team looked at 3-D pixels of the brain (voxels) to get a full picture of what was happening in the whole brain and how different areas were interacting.

Continuing the airport analogy, Palaniyappan said that if they had looked only at the brain’s language area it would have been like only walking into one airport, and not understanding how the reduced traffic in that airport was influencing air traffic at other surrounding airports.

“We went in without any expectations, and searched the whole brain,” said Dr. Palaniyappan. “This unique approach allowed us to get a picture of the forest rather than a picture of the tree.”

The study is the first to use this kind of imaging study in patients with acute psychosis who had a wide variety and severity of clinical symptoms.

“Because of the uniqueness of this patient group, we made sure ample time was given to explain the procedure and what was expected,” said Joe Gati, acting director of the Western Centre for Functional and Metabolic Mapping at Robarts.

“The protocol was kept brief and communication with the subject was maintained throughout the procedure. We also ensured that the imaging protocol was optimized to prioritize data collection, keep noise levels as low as possible, and to minimize the amount of time research subjects were required to be in the MRI.”

How does the human brain intermediate between behavioral symptoms and the development of brain diseases? Which brain areas are involved in this process? Can we chart these areas’ functional characteristics and structural organization?

Researchers studying brain diseases often observe that brain signals are on the one hand associated with behavioral symptoms, and on the other hand linked to disease status. Conventionally, the former is called an independent variable, the latter is called a dependent variable (or an outcome), and the brain areas interposed in-between are called mediators.

A central problem in neural mediation analysis is to identify which brain regions are positioned along the pathway between behavioral symptoms and disease status. Equally important is to quantify the effect of each identified brain area on developing the disease and to determine its relative prominence in the mediation system.

Disorganization symptoms, such as bizarre thoughts and behaviors, are considered to be associated with conversion to psychosis among individuals at clinical high risk (CHR); empirical studies have shown a significantly higher hazard ratio for psychosis onset in CHR subjects with higher disorganization symptoms at baseline (Cannon et al., 2008; Demjaha et al., 2012; Carrión et al., 2013).

Yet, as properties associated with a mental disorder, the disorganization symptoms and disease development are reflected by the measured brain signals. Probing into the neural basis of human behavior and disease development, mediation analysis can help us to understand the functional attributes and structural topography of the brain areas that potentially mediate behavioral symptoms and disease development. But it can only do so by first charting the neural pathways that make brain mediation possible.

A beginning in this direction can be made by identifying and isolating neural mediators that are interposed between behavioral symptoms and disease development (see Fig. 1). Central to this enquiry is a high-dimensional brain mediation analysis: examining hundreds of thousands of brain areas to find a subset of potential mediators.

To uncover high-dimensional functional neural mediators with binary outcomes (e.g., whether one has a full-blown psychotic disorder or not), one, however, must confront several challenges. First, although existing mediation models have made the search for mediators fruitful, they are not suitable for studying high-dimensional mediation analysis with binary outcomes.

For example, existing multi-level mediation models assume that the outcomes are continuously distributed (Chén et al., 2018; Geuter et al., 2020; Huang and Pan, 2016; VanderWeele and Vansteelandt, 2014); mediation frameworks concerning binary outcomes are at present restricted to a relatively small number of mediators (VanderWeele and Vansteelandt, 2014; Nguyen, 2016); high-dimensional mediation models whose outcomes are not normally distributed do not have a closed form solution (therefore it is difficult to estimate parameters analytically, as, for example, in (Chén et al., 2018)).

Second, although functional mediation analysis (Lindquist, 2012) has considerably advanced knowledge about the functional signal organization of the brain in relation to independent and outcome variables, it remains unclear whether it is suitable for analyzing high-dimensional brain data, and if so, how the underlying data configuration, such as the sample size and noise level, would affect parameter estimation. In parallel, its efficacy needs to be evaluated for brain disease studies.

Third, signals from brain mediators could be orthogonal or non-orthogonal. Whether and how their orthogonality would affect mediation analysis is an as-of-yet less-well-charted area. If not properly treated, this set of circumstances could generate inconsistent results and confusing interpretations. Finally, the search for functional neural mediators among subjects with severe behavioral symptoms raises the question of which mediators are positively, and which are negatively, mediating the development of brain disorders.

Fig. 1
Fig. 1
The study layout of the neural mediation analysis.(a) We considered a sample of 263 subjects recruited from eight study sites across the United States and Canada who met criteria for a prodromal risk syndrome at the point of recruitment and had been clinically followed up for two years as part of the NAPLS-2 project. During the follow-up period, 25 subjects developed a full-blown psychotic disorder (CHR convertors); 238 did not (CHR non-convertors). (b) The behavioral symptoms of convertors were significantly more severe than those of non-convertors. (c) The neural mediation analysis investigated which brain regions were intermediating between psychosis symptoms and disease status. Once neural mediators were identified, one could further quantify the mediation effect of each mediator to determine its relative prominence in the mediation system. (d) Both convertors and non-convertors received an eyes-open resting-state functional magnetic resonance imaging (fMRI) scan at the point of recruitment. (e) Resting-state brain activities from both convertor and non-convertor samples were plotted along 130,992 brain areas. The red shade represented brain signals stacked across the convertor group along the whole-brain space and the blue shade represented those from the non-convertor group.

To address these questions, here we propose a high-dimensional functional mediation model. Through simulation studies and empirical data analysis, we demonstrate that the model may be useful to (a) analyze large-scale intermediating brain signals (e.g., resting-state brain activities from hundreds of thousands of voxels); (b) distinguish distinctive functional brain regions between different groups in relation to behavior symptoms; (c) quantify each neural mediator’s effect on disease outcome; and (d) identify and separate brain areas that are potentially positively and negatively mediating brain disorders.

In clinical practice, one assumes that an irregular change of brain signals can first cause prodromal signs and symptoms, followed in some cases by later conversion to psychosis. In this paper, we aimed at studying the influence of the underlying brain signals on the link between two directly and clinically observable sets of variables: prodromal signs and symptoms on the one hand, and conversion state on the other hand.

The framework we designed to map the pathways contained directed arrows. The arrows clarified that the statistical model was a mediation one they did not suggest definitive causal flows from prodromal signs via brain areas towards conversion status (see Figs. 1 and ​and2).2). When confusion about the causal direction arises, one can interpret the identified neural mediators as brain areas that are jointly associated with behavioral symptoms and psychosis conversion.

In other words, the neural mediators exclude brain areas that are associated with conversion, but that are not associated with prodromal symptoms, and vice versa. One should also note that in cases where the brain signals first have an effect on the symptoms and then on the disease status (namely when the symptoms are the mediator), the identified brain regions are identical to the alpha atlas estimated by the proposed model (see Results).

Although there are overlaps between the two models, the interpretations are different. The key differences between brain areas identified by these two models are (1) the identified brain areas in the current study are potential neural mediators whereas those identified using the other model (where the symptom is treated as the mediator) are multivariate independent variables; and (2) the neural mediators from the present study are a subset of brain areas identified using the other model.

In the Supporting Information, we extend the proposed model to causal mediation setting using counterfactuals; additionally, we discuss how to interpret the model when causal inference is concerned; in cases where brain signals can be manually controlled (e.g., via transcranial magnetic stimulation (TMS)), under some identification conditions, the proposed model may unveil potential causal direct effect and indirect effect on the odds ratio scale.

Fig. 2
Fig. 2
A schematic representation of mediation analysis. (a) Univariate mediation analysis. The circles indicate an independent variable, a univariate mediator, an outcome variable, and covariates. The arrows denote pathways. The letter α denotes the effect from the independent variable to the mediator, after accounting for the covariate effect. The letter β denotes the effect of the mediator on the outcome, after controlling the independent variable and covariates. The letter γ denotes the effect from the independent variable to the outcome, after accounting for the covariate effect. (b)Multivariate neural mediation analysis. Each circle within the brain represents a potential neural mediator. The arrows denote pathways. The letter αi (1≤i≤V) denotes the effect from the independent variable to the ith neural mediator (represented by a red circle). The letter βi denotes the effect of the neural mediator on the outcome, after controlling the independent variable and covariates. The letter γ indicates the direct effect from the independent variable to the outcome, after accounting for the covariate effect.

reference link :

Original Research: Open access.
Conceptual disorganization and redistribution of resting-state cortical hubs in untreated first-episode psychosis: A 7T study” by Avyarthana Dey, Kara Dempster, Michael MacKinley, Peter Jeon, Tushar Das, Ali Khan, Joe Gati & Lena Palaniyappan. npj Schizophrenia


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