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Striatal connectopic maps link to functional domains across psychiatric disorders.

ABSTRACT: Transdiagnostic approaches to psychiatry have significant potential in overcoming the limitations of conventional diagnostic paradigms. However, while frameworks such as the Research Domain Criteria have garnered significant enthusiasm among researchers and clinicians from a theoretical angle, examples of how such an approach might translate in practice to understand the biological mechanisms underlying complex patterns of behaviors in realistic and heterogeneous populations have been sparse. In a richly phenotyped clinical sample (n = 186) specifically designed to capture the complex nature of heterogeneity and comorbidity within- and between stress- and neurodevelopmental disorders, we use exploratory factor analysis on a wide range of clinical questionnaires to identify four stable functional domains that transcend diagnosis and relate to negative valence, cognition, social functioning and inhibition/arousal before replicating them in an independent dataset (n = 188). We then use connectopic mapping to map inter-individual variation in fine-grained topographical organization of functional connectivity in the striatum-a central hub in motor, cognitive, affective and reward-related brain circuits-and use multivariate machine learning (canonical correlation analysis) to show that these individualized topographic representations predict transdiagnostic functional domains out of sample (r = 0.20, p = 0.026). We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.

SUBMITTER: Mulders PCR

PROVIDER: S-EPMC9747785 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Striatal connectopic maps link to functional domains across psychiatric disorders.

Mulders Peter C R PCR van Eijndhoven Philip F P PFP van Oort Jasper J Oldehinkel Marianne M Duyser Fleur A FA Kist Josina D JD Collard Rose M RM Vrijsen Janna N JN Haak Koen V KV Beckmann Christian F CF Tendolkar Indira I Marquand Andre F AF

Translational psychiatry 20221213 1

Transdiagnostic approaches to psychiatry have significant potential in overcoming the limitations of conventional diagnostic paradigms. However, while frameworks such as the Research Domain Criteria have garnered significant enthusiasm among researchers and clinicians from a theoretical angle, examples of how such an approach might translate in practice to understand the biological mechanisms underlying complex patterns of behaviors in realistic and heterogeneous populations have been sparse. In ...[more]

PMID: 36513630

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Project description:Introduction The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders. Methods We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject. Results and discussion We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.

Project description:Lifestyle changes involving high caloric diet, sedentary life, and/or persistent stress appear to play a role in the onset of metabolic syndrome (MetS), which is characterized by hyperglycemia, hyperinsulinemia, dyslipidemia, obesity, and type-2 diabetes. MetS in the long run appears to have cerebrovascular consequences, resulting in neuropsychiatric disorders. However, there is a paucity of molecular studies using relevant animal models of metabolic disorder-induced neuropsychiatric disorders. Here, we tried to model the clinical conditions of MetS by feeding C57bl/6 Ncrl mice a high fructose diet (Hfr) for almost 12 months. Random glucose, fasting glucose, glucose tolerance, and triglycerides did not differ significantly between the Hfr diet and Control diet (Cont) groups even after a year on the Hfr diet, except for the accelerated aging-like phenotype in coat color and shiny texture. However, exposure to chronic stress for a week induced metabolic disorder as reflected in high post-prandial blood glucose in the Hfr group, and subsequently mood disorder (depression and anxiety) (p<0.01) and cognitive disorder (p<0.01) as indicated by the behavioral tests. To uncover the underlying molecular mechanisms in the prefrontal cortex, a transcriptomic study was performed. The data analysis revealed hundreds of differentially expressed genes (DEGs) between the groups (i.e., HfrUST vs CoUST = 348 genes; CoST vs CoUST = 496 genes; HfrST vs HfrUST =226 genes and HfrST vs CoST= 18 genes). The pattern of gene expression was strikingly different in the HfrST group compared to the Ctrl group, thus correlating the phenotype, i.e. MetS induced mood and cognitive disorders. Pathway analysis of the DEGs indicated perturbations in cellular metabolism, inflammation, innate immunity, neurogenesis, vasculogenesis, ion channels, and neuronal signaling. In addition, altered epigenetic regulators appear to mediate the stress-induced precipitation of metabolic and neuropsychiatric disorders. The outcome of our study supports the hypothesis of disease susceptibility due to lifestyle changes involving a high-calorie diet and chronic stress.

Project description:Delineating the neuropathological characteristics across psychiatric disorders is critical for understanding their pathophysiology. The purpose of this study was to investigate common and distinct brain functional abnormalities across psychiatric disorders by using functional stability, a recently developed dynamic functional connectivity approach. Resting-state functional magnetic resonance imaging data were collected from a transdisease sample of healthy controls (n = 115) and individuals with schizophrenia (SZ) (n = 47), bipolar disorder (BD) (n = 44), and attention deficit/hyperactivity disorder (ADHD) (n = 40). Functional stability of each voxel was calculated by measuring the concordance of dynamic functional connectivity over time. Differences in functional stability among the four groups were assessed voxel-wisely. Compared to healthy controls, individuals with SZ demonstrated a distributed pattern of higher functional stability in the bilateral inferior temporal gyrus yet lower stability in the bilateral calcarine sulcus and left insula; individuals with BD only manifested local higher stability in the left inferior temporal gyrus; no differences were found between ADHD and healthy individuals. Notably, individuals with SZ and BD had common higher functional stability in the left inferior temporal gyrus, whereas higher functional stability in the right inferior temporal gyrus and lower stability in the bilateral calcarine sulcus and left insula were unique abnormalities in individuals with SZ. Additionally, direct comparisons between disorders revealed that individuals with SZ showed lower functional stability in the right calcarine sulcus compared to those with BD and higher stability in the left inferior temporal gyrus compared to those with ADHD. However, no significant associations between functional stability and clinical symptoms were observed. Our findings suggest that the functional stability approach has the potential to be extended to the domain of psychiatry and encourage further investigations of shared and unique neuropathology of psychiatric disorders.

Project description:Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.

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Striatal connectopic maps link to functional domains across psychiatric disorders.

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Striatal connectopic maps link to functional domains across psychiatric disorders.

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