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Mapping the Interaction between Semantic Representation and Control Systems: The Controlled Semantic Cognition

Periodic Reporting for period 1 - MapInCSC (Mapping the Interaction between Semantic Representation and Control Systems: The Controlled Semantic Cognition)

Reporting period: 2020-08-01 to 2022-07-31

Patient conditions vary greatly, requiring the clinician to use their expertise to manage these differences. They must determine whether variations signify different diseases requiring unique treatments or are just expected differences within the same disorder requiring similar treatment approaches. There are three types of variations: categorical, dimensional, and random noise. Categorical variation refers to clear-cut patient differences, making it easy to understand and research. This is what traditional diagnosis methods are based on. Dimensional variations involve measurable factors such as the severity of a disorder or common dimensions that cut across different conditions, like blood pressure and body temperature. Recent studies have highlighted the benefits of multidimensional behavioural geometries over categorising every variation in a patient's behaviour. These methods allow for a more nuanced understanding by mapping patients' symptoms onto multidimensional scales, capturing broad distinctions, subtle variations, mixed features, and changes over time. Multidimensional approaches help identify common symptoms across different disorders that are important for predicting outcomes and could potentially be treated, like apathy in neurodegenerative diseases.
This study focused on the challenge of how to relate behavioural dimensions to underlying neurological changes in patients. The study applied multidimensional behavioural geometries to represent the variations in patients' symptoms and relate them to atrophy patterns. Frontotemporal dementia (FTD) cortical and subcortical syndromes, characterised by progressive cognitive, behavioural, language, and motor deficits, were used as test cases. The investigation achieved this aim by 1) Using principal component analysis (PCA) to extract the multidimensional behavioural geometry from the Mini-Language State Examination (MLSE) battery, thereby capturing the continuum-graded language variations across the FTD spectrum. 2) Employing three approaches to reveal the brain-behaviour relationships: (i) classic univariate voxel-based morphometry correlations; (ii) PCA of atrophy maps to extract coherent brain patterns and thus test which of these disease-defined anatomical dimensions relate to the behavioural dimensions; and (iii) exploring whether variation on the behavioural dimensions relates to the integrity of one versus several brain regions, and thus shifting from univariate to multivariate regression models to capture the latter cognition-to-distributed network relationships, when they exist.
Our findings revealed more distributed atrophy patterns for motor-speech/phonology and syntax factors, while semantics is more localised in the anterior part of the inferior temporal lobe. Importantly, while many studies rely on univariate correlations to investigate brain correlations in cognitive disorders, our findings indicate that this approach falls short of capturing the complex relationships between behaviour and the brain. Given the distributed and interactive nature of language functions, isolating specific language processes in the brain poses a great challenge, affecting how language is altered, reflected in the heterogeneity of language deficits in FTD. Incorporating multivariate analysis becomes essential to capture these intricate associations and identify common and distinct neural mechanisms of language. In conclusion, other aspects of language besides semantics rely on various brain regions, demonstrating the complex multidimensional architecture of language functions.
First, we extracted three distinct language components: motor-speech/phonology, semantics, and syntax. These three-factor model explained 66.74% of the linguistic variations across FTD patients and validated the MLSE’s sensitivity to language degradation. Second, we found that brain atrophy varied across thirteen dimensions, including left/right frontal, temporal, and parietal areas. By situating patients along the multiple dimensions, we observed that patients might present with different combinations of brain atrophies and language impairments, suggesting a continuum brain-behaviour. Single correlation analysis showed strong relationships between semantic factor and brain components in the anterior temporal lobe (ATL) and moderate correlations between motor-speech/phonology and syntax factors with various brain components. Lastly, multiple linear regression revealed that regions in several atrophic dimensions better predicted motor-speech/phonology and syntax, indicating the interactive and distributed nature of language functions in the brain. While the anterior temporal lobe (ATL) uniquely predicted the semantic performance, suggesting a more focal involvement of the ATL in semantic processing.
An important aspect of my work has been sharing the results with the scientific community. I achieved this by presenting findings at scientific conferences (e.g. BNA2023 Neuroscience Festival, CLS annual symposium 2022, ISFTD 2022, CNS2022 - New Horizons, SNL 2020), preparing publications (two under review), presentations on internal and external meetings and collaborating with other fellows.
Progress beyond the state of the art in my research has been achieved by adopting transdiagnostic and multidimensional approaches in the study of neurodegenerative disorders. This paradigm shift acknowledges the heterogeneity and symptom overlap present in these disorders, moving away from traditional categorical groupings and enabling a more comprehensive understanding of the diseases under investigation. Despite the challenges brought about by COVID-19, my research persevered through remote work, virtual collaboration, and modified protocols.
The impact of this project extends to my potential and future career prospects. The training and experience gained in transdiagnostic approaches position me at the forefront of innovative research methodologies in neurodegenerative disorders, opening up opportunities for collaboration and advancement. The MSCA fellowship has enhanced my management skills, making me a valuable asset in fostering research. Whether in my current role or future endeavours, I am well-prepared to drive scientific advancements and positively impact the field of neurodegenerative disorders.
From a socio-economic perspective, this work contributes to the innovation capacity in neurodegenerative disorders by improving understanding and diagnosis, leading to better patient care and management strategies. The research addresses the need for more accurate assessments of cognitive impairments, enabling tailored interventions and therapies, ultimately enhancing the quality of life for individuals affected by these disorders. Moreover, the project aligns with the priorities of European healthcare policies, advancing precision medicine and personalized healthcare. The results of this research are of great interest to various stakeholders, including healthcare professionals, researchers, and pharmaceutical companies specializing in neurodegenerative diseases.
PCA on MLSE measures. Underlying language dimensions of variance in FTD-PPA
ROI-based multiple regression analysis to predict the emergent PC language performance
Voxel-based correlation analysis in the FTD patients
Brain atrophic PC in FTD patients that significantly correlate with language PC