Psychiatric Epidemiology and Neuroscience Unite in the Pursuit of Reformulated Schizophrenia Nosologies

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Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. Comparison of the results are based on model evidence, structural similarities and the number of model estimations needed during search. An analytical approach using Bayesian model reduction (BMR) for efficient network discovery is already available for DCM. Comparing model search methods we found that topological algorithms often outperform analytical methods for single-subject analysis and achieve similar results for recovering common network properties of the winning model family, or set of models, obtained by multi-subject family-wise analysis. However, network search methods show their limitations in higher level statistical analysis of parametric empirical Bayes. Optimizing such linear modeling schemes the BMR methods are still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience

Identifying Functional Imaging Markers in Psychosis Using fMRI

Major types of psychotic disorders include schizophrenia (SCZ), bipolar disorder (BP) and schizoaffective disorder (SZA). These disorders have profound and overlapping symptoms with marked cognitive deficits, and their diagnosis relies on symptom clusters. The treatments for psychosis are usually focused on positive symptoms such as delusions and hallucinations. Although cognitive impairments underlie both positive and negative symptoms, functional brain imaging biomarkers that can reliably predict a patient\u27s cognitive deficits are still lacking. Therefore, this project used functional MRI to explore the feasibility of using functional connectivity (FC) to predict cognitive performance. A total of 207 subjects (BP: 79, SZ/SZA: 48, and HC: 80) with high functional MRI image (fMRI) quality (SNR\u3e 100, motion \u3c 0.3) were selected from the McLean MATRICS dataset. Subjects were divided into a discovery cohort (n=104) and an age, gender, and head motion matched validation cohort (n=103). The hypothesis was that FC could predict cognitive performance in the discovery cohort and that the prediction models could be generalized to the validation cohort. The connectomes for each subject were obtained by calculating the whole- brain connectivity using networks from the individualized functional parcellation as region of interests (ROIs). Models were trained to predict the 8 cognitive scores in the discovery cohort, respectively. The generalizability of these models was tested by applying these models to the validation cohort. The trained models were able to predict 6 out of 8 cognitive scores using a LOOCV procedure. Models for working memory, composite score and attention score could be generalized to the validation cohort. A total of 35 FC features were identified as important for predicting performance in these cognitive domains. Significant differences between patients and controls were found for 13 of these features when considered individually. In summary, this project has established a framework for biomarker discovery that may have clinical relevance for the diagnosis of psychosis early in the disease process by providing possible FC features that can be detected using fMRI and may help guide therapeutic interventions. The identified biomarkers also provide convergent evidence for network dysfunction in psychosis and suggest personalized treatment targets

Noise-induced synchronization and anti-resonance in excitable systems; Implications for information processing in Parkinson's Disease and Deep Brain Stimulation

We study the statistical physics of a surprising phenomenon arising in large networks of excitable elements in response to noise: while at low noise, solutions remain in the vicinity of the resting state and large-noise solutions show asynchronous activity, the network displays orderly, perfectly synchronized periodic responses at intermediate level of noise. We show that this phenomenon is fundamentally stochastic and collective in nature. Indeed, for noise and coupling within specific ranges, an asymmetry in the transition rates between a resting and an excited regime progressively builds up, leading to an increase in the fraction of excited neurons eventually triggering a chain reaction associated with a macroscopic synchronized excursion and a collective return to rest where this process starts afresh, thus yielding the observed periodic synchronized oscillations. We further uncover a novel anti-resonance phenomenon: noise-induced synchronized oscillations disappear when the system is driven by periodic stimulation with frequency within a specific range. In that anti-resonance regime, the system is optimal for measures of information capacity. This observation provides a new hypothesis accounting for the efficiency of Deep Brain Stimulation therapies in Parkinson's disease, a neurodegenerative disease characterized by an increased synchronization of brain motor circuits. We further discuss the universality of these phenomena in the class of stochastic networks of excitable elements with confining coupling, and illustrate this universality by analyzing various classical models of neuronal networks. Altogether, these results uncover some universal mechanisms supporting a regularizing impact of noise in excitable systems, reveal a novel anti-resonance phenomenon in these systems, and propose a new hypothesis for the efficiency of high-frequency stimulation in Parkinson's disease

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks

A Functional Role for Neural Columns: Resolving F2 Transition Variability in Stop Place Categorization

Documented examples from neuroethology have revealed species-specific neural encoding mechanisms capable of mapping highly variable, but lawful, visual and auditory inputs within neural columns. By virtue of the entire column being the functional unit of both representation and processing, signal variation is collectively ‘absorbed’, and hence normalized, to help form natural categories possessing an underlying physically-based commonality. Stimulus-specific ‘tolerance ranges’ define the limits of signal variation, effectively shaping the functionality of the columnar-based processing. A conceptualization for an analogous human model utilizing this evolutionarily conserved neural encoding strategy for signal variability absorption is described for the non-invariance issue in stop place perception

From minimal dependencies to sentence contexts: neural correlates of agreement processing

289 p.Language comprehension is incremental, involving the integration of formal and conceptual information from different words, together with the need to resolve conflicting cues when unexpected information occurs. However, despite the extensive amount of findings regarding how the brain deals with these information, two essential and still open questions are 1whether the neural circuit(s) for coding syntactic and semantic information embedded in our linguistic code are the same or different, and 2whether the possible interaction(s) between these two different types of information leaves a trace in the brain response. The current thesis seeks to segregate the neuro-anatomical substrates of these processes by taking advantage of the Spanish agreement system. This system comprised those procedural mechanisms concerning the regular assignment of the number [singular, plural], person [first, second and third] and/or gender [feminine, masculine] information, associated with different sentence constituents. Experimental manipulations concerning different agreement features and the elements involved in an agreement relation, allowed us to characterize the neural network underlying agreement processing. This thesis comprised five experiments: while experiments I and II explored nominal dependencies in local as well as non-local relations, experiments III, IV and V explored subject-verb relations in a more complex sentence context. To distinguish between purely syntactic mechanisms and those where semantic and syntactic factors would interact during language comprehension, different types of agreement relations and/or agreement features were manipulated in well- and ill-formed constructions. The interaction effect between the different factors included in each experiment was always the critical comparison. In general, our results include firstly a functional dissociation between well-formed and ill-formed constructions: while ill-formed constructions recruited a bilateral distributed fronto-parietal network associated to conflict monitoring operations, not language specific, well-formed constructions recruited a left lateralized fronto-temporo-parietal network that seems to be specifically related to different aspects of phrase and sentence processing. Secondly, there was an anterior to posterior functional gradient associated to the middle and superior temporal cortex that consistently appears across experiments. Specifically, while the posterior portion of the left MTG-STG seems to be related to the storage and retrieval of lexical and morpho-syntactic information, the anterior portion of this region was related to syntactic-combinatorial building mechanisms. Critically, in the most anterior part of the left temporal cortex, corresponding with the middle and superior temporal pole, form-to-meaning mapping processes seems to be represented. Thirdly, the response of the left temporal cortex appears to be controlled by left inferior frontal regions (LIFG). Finally, left parietal regions such us the angular gyrus showed increased activation for those manipulations involving semantic factors (e.g., conceptual gender and Unagreement constructions), highlighting its crucial role in the processing of different types of semantic information (e.g., conceptual integration and semantic-discourse integration). Overall, these findings highlight the sensitivity of the agreement system to syntactic and semantic factors embedded into an agreement relation, opening new windows to the study of agreement computation and language comprehension.bcbl: basque center on cognition, brain and languag