{"id":{"repo_id":"tuebingen","oai_identifier":"oai:publikationen.uni-tuebingen.de:10900/172348"},"canonical_url":"https://search.dev.ndltd.org/etd/tuebingen/oai:publikationen.uni-tuebingen.de:10900/172348","repository":{"repo_id":"tuebingen","name":"Universität Tübingen","base_url":"https://publikationen.uni-tuebingen.de/oai/request"},"display":{"title":"Mapping Age- and ALS-Related Changes in the Sensorimotor Cortex with Multivariate 7T-fMRI Analyses","abstract":"Functional Magnetic Resonance Imaging (fMRI), particularly at ultra-high-field strengths such as 7-Tesla (7T), has revolutionized neuroscience by enabling detailed exploration of the human brain’s functional architecture. This thesis leverages the advanced spatial resolution of 7T-fMRI to investigate the sensorimotor cortex, focusing on age-related changes and disease-specific alterations associated with Amyotrophic Lateral Sclerosis (ALS). While the unprecedented resolution of 7T-fMRI allows for the analysis of fine-grained features, such as cortical columns and laminar structures, the high dimensionality and complexity of the data necessitate sophisticated analytical methods. This work employs 7 T task-based fMRI with advanced multivariate techniques, including Robust Shared Response Modeling (rSRM), Columnar Shared Response Modeling (C-SRM) (introduced as a novel method), and Partial Least Squares Regression (PLSR), to address the challenges posed by high-dimensional fMRI data. SRM and C-SRM are utilized to align functional data across participants and examine fine-scale neural organization, particularly at the columnar level, while PLSR links neural activity to clinical and behavioral outcomes. These approaches enable the identification of both shared and individual-specific neural patterns, offering a nuanced understanding of functional changes in the sensorimotor cortex. In the context of aging, rSRM and C-SRM show that the hierarchical layout of Brodmann areas (BA) 3b → 1 → 2 is preserved in older adults, yet digit representations become less precise. In BA1, the optimal number of functional columns drops relative to young adults, indicating enlarged, less se- lective columns; while BA3b remains stable. These findings reveal a subtle dedifferentiation—blurred maps but an intact hierarchy—consistent with compensatory pooling of sensory inputs. In ALS, rSRM combined with PLSR distinguishes patients from controls with high accuracy based on task-evoked BOLD patterns. Connectivity-derived latent variables outperform activation maps in clustering disease onset site and staging, and they exhibit an atopographic signature: foot and face regions of MI track progression regardless of the initial symptom locus. The data suggest an early, network-wide hyper-connective compensation that collapses as degeneration advances. Together, these results validate advanced alignment and dimensionality-reduction strategies for extracting fine grained insights from 7 T data. They establish enlarged columns as a fingerprint of healthy ageing and identify network-level connectivity markers for ALS staging—outcomes that can guide larger multicentre, multimodal studies and inform therapeutic efforts aimed at preserving sensorimotor function across the lifespan and in neurodegenerative disease.","abstract_html":"Functional Magnetic Resonance Imaging (fMRI), particularly at ultra-high-field strengths such as 7-Tesla (7T), has revolutionized neuroscience by enabling detailed exploration of the human brain’s functional architecture. This thesis leverages the advanced spatial resolution of 7T-fMRI to investigate the sensorimotor cortex, focusing on age-related changes and disease-specific alterations associated with Amyotrophic Lateral Sclerosis (ALS). While the unprecedented resolution of 7T-fMRI allows for the analysis of fine-grained features, such as cortical columns and laminar structures, the high dimensionality and complexity of the data necessitate sophisticated analytical methods. This work employs 7 T task-based fMRI with advanced multivariate techniques, including Robust Shared Response Modeling (rSRM), Columnar Shared Response Modeling (C-SRM) (introduced as a novel method), and Partial Least Squares Regression (PLSR), to address the challenges posed by high-dimensional fMRI data. SRM and C-SRM are utilized to align functional data across participants and examine fine-scale neural organization, particularly at the columnar level, while PLSR links neural activity to clinical and behavioral outcomes. These approaches enable the identification of both shared and individual-specific neural patterns, offering a nuanced understanding of functional changes in the sensorimotor cortex. In the context of aging, rSRM and C-SRM show that the hierarchical layout of Brodmann areas (BA) 3b → 1 → 2 is preserved in older adults, yet digit representations become less precise. In BA1, the optimal number of functional columns drops relative to young adults, indicating enlarged, less se- lective columns; while BA3b remains stable. These findings reveal a subtle dedifferentiation—blurred maps but an intact hierarchy—consistent with compensatory pooling of sensory inputs. In ALS, rSRM combined with PLSR distinguishes patients from controls with high accuracy based on task-evoked BOLD patterns. Connectivity-derived latent variables outperform activation maps in clustering disease onset site and staging, and they exhibit an atopographic signature: foot and face regions of MI track progression regardless of the initial symptom locus. The data suggest an early, network-wide hyper-connective compensation that collapses as degeneration advances. Together, these results validate advanced alignment and dimensionality-reduction strategies for extracting fine grained insights from 7 T data. They establish enlarged columns as a fingerprint of healthy ageing and identify network-level connectivity markers for ALS staging—outcomes that can guide larger multicentre, multimodal studies and inform therapeutic efforts aimed at preserving sensorimotor function across the lifespan and in neurodegenerative disease.","abstract_has_math":false,"creators":["Kalyani, Avinash"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-11-19","date_published":"2025-11-19","updated_at":"2026-08-21T22:21:56Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10900/172348"],"render_values":[{"text":"hdl:10900/172348","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"source_record":{"url":"https://publikationen.uni-tuebingen.de/oai/request?verb=GetRecord&metadataPrefix=mets&identifier=oai%3Apublikationen.uni-tuebingen.de%3A10900%2F172348","prefix":"mets"},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-11-19"]},{"key":"dc:type","label":"Dc Type","values":["PhDThesis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10900/172348"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Functional Magnetic Resonance Imaging (fMRI), particularly at ultra-high-field strengths such as 7-Tesla (7T), has revolutionized neuroscience by enabling detailed exploration of the human brain’s functional architecture. This thesis leverages the advanced spatial resolution of 7T-fMRI to investigate the sensorimotor cortex, focusing on age-related changes and disease-specific alterations associated with Amyotrophic Lateral Sclerosis (ALS). While the unprecedented resolution of 7T-fMRI allows for the analysis of fine-grained features, such as cortical columns and laminar structures, the high dimensionality and complexity of the data necessitate sophisticated analytical methods. This work employs 7 T task-based fMRI with advanced multivariate techniques, including Robust Shared Response Modeling (rSRM), Columnar Shared Response Modeling (C-SRM) (introduced as a novel method), and Partial Least Squares Regression (PLSR), to address the challenges posed by high-dimensional fMRI data. SRM and C-SRM are utilized to align functional data across participants and examine fine-scale neural organization, particularly at the columnar level, while PLSR links neural activity to clinical and behavioral outcomes. These approaches enable the identification of both shared and individual-specific neural patterns, offering a nuanced understanding of functional changes in the sensorimotor cortex. In the context of aging, rSRM and C-SRM show that the hierarchical layout of Brodmann areas (BA) 3b → 1 → 2 is preserved in older adults, yet digit representations become less precise. In BA1, the optimal number of functional columns drops relative to young adults, indicating enlarged, less se- lective columns; while BA3b remains stable. These findings reveal a subtle dedifferentiation—blurred maps but an intact hierarchy—consistent with compensatory pooling of sensory inputs. In ALS, rSRM combined with PLSR distinguishes patients from controls with high accuracy based on task-evoked BOLD patterns. Connectivity-derived latent variables outperform activation maps in clustering disease onset site and staging, and they exhibit an atopographic signature: foot and face regions of MI track progression regardless of the initial symptom locus. The data suggest an early, network-wide hyper-connective compensation that collapses as degeneration advances. Together, these results validate advanced alignment and dimensionality-reduction strategies for extracting fine grained insights from 7 T data. They establish enlarged columns as a fingerprint of healthy ageing and identify network-level connectivity markers for ALS staging—outcomes that can guide larger multicentre, multimodal studies and inform therapeutic efforts aimed at preserving sensorimotor function across the lifespan and in neurodegenerative disease."]},{"key":"dc:title","label":"Title","values":["Mapping Age- and ALS-Related Changes in the Sensorimotor Cortex with Multivariate 7T-fMRI Analyses"]}]}],"canonical_facts":{"dc:date.issued":["2025-11-19"],"dc:description.other":["Functional Magnetic Resonance Imaging (fMRI), particularly at ultra-high-field strengths such as 7-Tesla (7T), has revolutionized neuroscience by enabling detailed exploration of the human brain’s functional architecture. This thesis leverages the advanced spatial resolution of 7T-fMRI to investigate the sensorimotor cortex, focusing on age-related changes and disease-specific alterations associated with Amyotrophic Lateral Sclerosis (ALS). While the unprecedented resolution of 7T-fMRI allows for the analysis of fine-grained features, such as cortical columns and laminar structures, the high dimensionality and complexity of the data necessitate sophisticated analytical methods. This work employs 7 T task-based fMRI with advanced multivariate techniques, including Robust Shared Response Modeling (rSRM), Columnar Shared Response Modeling (C-SRM) (introduced as a novel method), and Partial Least Squares Regression (PLSR), to address the challenges posed by high-dimensional fMRI data. SRM and C-SRM are utilized to align functional data across participants and examine fine-scale neural organization, particularly at the columnar level, while PLSR links neural activity to clinical and behavioral outcomes. These approaches enable the identification of both shared and individual-specific neural patterns, offering a nuanced understanding of functional changes in the sensorimotor cortex. In the context of aging, rSRM and C-SRM show that the hierarchical layout of Brodmann areas (BA) 3b → 1 → 2 is preserved in older adults, yet digit representations become less precise. In BA1, the optimal number of functional columns drops relative to young adults, indicating enlarged, less se- lective columns; while BA3b remains stable. These findings reveal a subtle dedifferentiation—blurred maps but an intact hierarchy—consistent with compensatory pooling of sensory inputs. In ALS, rSRM combined with PLSR distinguishes patients from controls with high accuracy based on task-evoked BOLD patterns. Connectivity-derived latent variables outperform activation maps in clustering disease onset site and staging, and they exhibit an atopographic signature: foot and face regions of MI track progression regardless of the initial symptom locus. The data suggest an early, network-wide hyper-connective compensation that collapses as degeneration advances. Together, these results validate advanced alignment and dimensionality-reduction strategies for extracting fine grained insights from 7 T data. They establish enlarged columns as a fingerprint of healthy ageing and identify network-level connectivity markers for ALS staging—outcomes that can guide larger multicentre, multimodal studies and inform therapeutic efforts aimed at preserving sensorimotor function across the lifespan and in neurodegenerative disease."],"dc:identifier":["hdl:10900/172348"],"dc:title":["Mapping Age- and ALS-Related Changes in the Sensorimotor Cortex with Multivariate 7T-fMRI Analyses"],"dc:type":["PhDThesis"]},"updated_at":"2026-08-21T22:21:56Z"}