Kiran, SwathiFalconer, Isaac2025-03-262025-03-262026https://hdl.handle.net/2144/499412026The recovery of language abilities post-stroke follows diverse trajectories influenced by various factors including lesion characteristics, overall brain health, demographics, and social factors. While many studies have examined static functional connectivity (sFC, i.e., time-invariant inter-regional correlations of intrinsic neural activity), little attention has been given to the influence of short timescale brain dynamics on recovery. While static functional connectivity provides a snapshot of brain function, dynamic functional connectivity (dFC) analyses allow for the detection of transient states and examination of moment-to-moment fluctuations in functional connections. Very few studies have applied this type of approach to post-stroke aphasia populations, but one such study found a specific connectivity state associated with greater treatment response, suggesting that these may be informative techniques for post-stroke aphasia research. In this thesis, I aim to address this gap by investigating (1) spatial and temporal patterns of dFC in people with aphasia (PWA) compared to healthy controls and its relationships with aphasia severity, (2) the potential of dFC to predict treatment-induced recovery, and (3) the relationship between temporal patterns of dFC and functional reorganization during recovery, using both empirical data and computational simulations to test a hypothesized mechanism for these relationships. A major focus of this work is on temporal metrics of dFC, rather than specific spatial features or network properties, particularly temporal variability (TV), which specifically measures the magnitude of fluctuations over time. Neural variability, measured using numerous approaches including TV of dFC, has recently emerged as an important factor related to cognition, behavior, and mental health. Spatial patterns and network properties of dFC were investigated as well and discussed in the context of previous findings. The first study (Chapter 1) investigated alterations in dFC due to stroke by comparing TV and fractional occupancy of (i.e., time spent in) connectivity states between PWA and healthy controls. Additionally, relationships between each of these measures and aphasia severity were investigated. PWA were found to have reduced TV in language network regions compared to healthy controls and spent more time in a highly integrated state (state 3) with low modularity (i.e., little segregation between locally specialized communities). Higher TV was also associated with less severe aphasia, particularly in PWA with larger lesions. Although state 3 represents altered connectivity in PWA, those who spent more time in this state were no more severe than those who spent less time in it, suggesting that it is not simply a state defined by stroke-related dysfunction but may also (or instead) represent compensatory and adaptive changes.Given that dFC is altered in PWA and relates to aphasia severity, the second study (Chapter 2) aimed to determine whether it is also predictive of response to aphasia therapy. Consistent with the main finding of Chapter 1, PWA with higher TV at baseline were found to have greater treatment-induced gains in picture naming accuracy. A second temporal metric, community stability, which measures the tendency of the brain to maintain a given dFC configuration for longer lengths of time, was also positively associated with treatment response. For the dFC states analysis, participants with higher fractional occupancy of a higher modularity state showed greater improvement with treatment, consistent with the previous study mentioned above. Results of both the TV and dFC states analyses are consistent with the findings of Chapter 1, with PWA who are more normal-like (i.e., having higher TV and higher modularity) showing greater treatment gains. Given the apparent benefit of higher TV suggested by the findings of Chapters 1 and 2, we propose a mechanism linking higher TV to improved recovery: (1) Transient inter-regional synchronization facilitates plasticity in the synaptic connections between the respective regions, and (2) greater diversity of these transient synchronizations (i.e., higher TV) provides a greater variety of opportunities for plasticity mechanisms to reshape functional networks. The third study (Chapter 3) sought to investigate this proposed mechanism by testing the hypothesis that PWA with higher TV have a greater capacity for functional network reorganization, measured here as treatment-induced changes in sFC. These changes were quantified using global and node-level graph metrics computed from pre- and post-treatment scans of the same sample of PWA used in the second study. Node strength, a measure of a region’s overall connectivity with the rest of the brain, was found to decrease from pre- to post-treatment, and greater decreases were associated with greater behavioral treatment response. This decreased node strength may indicate a subtle shift toward segregation and local specialization that was not adequately captured by global-level segregation measures, which were not found to change significantly. Additionally, PWA with higher baseline TV showed greater decreases in node strength, supporting the hypothesis that TV facilitates functional network changes underlying recovery. Brain dynamics simulations tested two Hebbian-like plasticity rules and showed that only one of these was able to produce increases in network properties thought to be associated with better function (i.e., modularity and small-worldness). According to this rule, changes in average synaptic weights between a given pair of regions was inversely related to simultaneous coactivation of the respective regions with all other regions (referred to in this thesis as “mutual coactivation”). Additionally, simulations using this rule in which brain dynamics had greater TV showed greater increases in these network properties, consistent with the hypothesized mechanism. Ultimately, this work demonstrates a robust relationship between greater TV and better outcomes in post-stroke aphasia and provides support for an underlying mechanism of TV facilitating plasticity in PWA.en-USNeurosciencesAphasiaDynamic functional connectivityFunctional neuroimagingNeuroplasticityNeurorehabilitationStrokeThesis/Dissertation2025-03-180000-0002-6286-546X