Biology subjects

Cutts, S. A.

Publications and source records attributed to Cutts, S. A..

4 recordsLinked to original sources

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

neuroscience↗

bioRxiv2023

Temporal Variability of Brain-Behavior Relationships in Fine-Scale Dynamics of Edge Time Series

Most work on functional connectivity (FC) in neuroimaging data prefers longer scan sessions or greater subject count to improve reliability of brain-behavior relationships or predictive models. Here, we investigate whether systematically isolating moments in time can improve brain-behavior relationships and outperform full scan data. We perform optimizations using a temporal filtering strategy to identify time points that improve brain-behavior relationships across 58 different behaviors. We analyzed functional brain networks from resting state fMRI data of 352 healthy subjects from the Human Connectome Project. Templates were created to select time points with similar patterns of brain activity. Optimizations were performed to produce templates for each behavior that maximize brain-behavior relationships from reconstructed functional networks. With 10% of scan data, optimized templates of select behavioral measures achieved greater strength of brain-behavior correlations and greater transfer between groups of subjects than full FC across multiple cross validation splits of the dataset. Therefore, selectively filtering time points may allow for development of more targeted FC analyses and increased understanding of how specific moments in time contribute to behavioral prediction. Significance StatementIndividuals exhibit significant variations in brain functional connectivity, and these individual differences relate to variations in behavioral and cognitive measures. Here we show that the strength and similarity of brain-behavior associations across groups vary over time and that these relations can be improved by selecting time points that maximize brain-behavior correlations. By employing an optimization strategy for 58 distinct behavioral variables we find that different behaviors load onto different moments in time. Our work suggests new strategies for revealing brain signatures of behavior.

neuroscience↗

bioRxiv2021

Cortico-Subcortical Interactions in Overlapping Communities of Edge Functional Connectivity

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

neuroscience↗

bioRxiv2019

Perceptual decisions about object shape bias visuomotor coordination during rapid reaching and interception

Visual processing in parietal areas of the dorsal stream facilitates sensorimotor transformations for rapid movement. This action-related visual processing is hypothesized to play a distinct functional role from the perception-related processing in the ventral stream. However, it is unclear how the two streams interact when perceptual identification is a prerequisite to executing an accurate movement. In the current study, we investigated how perceptual decision-making involving the ventral stream influences arm and eye movement strategies. Participants (N = 26) moved a robotic manipulandum using right whole-arm movements to rapidly reach a stationary object or intercept a moving object on an augmented-reality display. On some blocks of trials, participants needed to identify the shape of the object (circle or ellipse) as a cue to either hit the object (circle) or move to a pre-defined location away from the object (ellipse). We found that during perceptual decision-making, there was an increased urgency to act during interception movements relative to reaching, which was associated with more decision errors. Faster hand reaction times were correlated with a strategy to adjust the movement post-initiation, and this strategy was more prominent during interception. Saccadic reaction times were faster and initial gaze lags and gains greater during decisions, suggesting that eye movements adapt to perceptual demands for guiding limb movements. Together, our findings suggest that the integration of ventral stream information with visuomotor planning depends on imposed (or perceived) task demands. New and NoteworthyVisual processing for perception and for action are thought to be mediated by two specialized neural pathways. Using a visuomotor decision-making task, we show that participants differentially utilized online perceptual decision-making in reaching and interception, and that eye movements necessary for perception influenced motor decision strategies. These results provide evidence that task complexity modulates how pathways processing perception versus action information interact during the visual control of movement.

neuroscience↗