Structurally informed models of directed brain connectivity
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Latent circuit inference from heterogeneous neural responses during cognitive tasks
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.
Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease
Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies.
Simultaneous tACS-fMRI reveals state- and frequency-specific modulation of hippocampal-cortical functional connectivity
Non-invasive indirect hippocampal-targeted stimulation is of broad scientific and clinical interest. Transcranial alternating current stimulation (tACS) is appealing because it allows oscillatory stimulation to study hippocampal theta (3–8 Hz) activity. We found that tACS administered during functional magnetic resonance imaging yielded a frequency-, mental state- and topologically-specific effect of theta stimulation (but not other frequencies) enhancing right (but not left) hippocampal-cortical connectivity during resting blocks but not during task blocks. Control analyses showed that this effect was not due to possible stimulation-induced changes in signal quality or head movement. Our findings are promising for targeted network modulations of deep brain structures for research and clinical intervention.
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
Resting-state fMRI reveals altered functional connectivity associated with resilience and susceptibility to chronic social defeat stress in mouse brain
Chronic stress is a causal antecedent condition for major depressive disorder and associates with altered patterns of neural connectivity. There are nevertheless important individual differences in susceptibility to chronic stress. How functional connectivity (FC) amongst interconnected, depression-related brain regions associates with resilience and susceptibility to chronic stress is largely unknown. We used resting-state functional magnetic resonance imaging (rs-fMRI) to examine FC between established depression-related regions in susceptible (SUS) and resilient (RES) adult mice following chronic social defeat stress (CSDS). Seed-seed FC analysis revealed that the ventral dentate gyrus (vDG) exhibited the greatest number of FC group differences with other stress-related limbic brain regions. SUS mice showed greater FC between the vDG and subcortical regions compared to both control (CON) or RES groups. Whole brain vDG seed-voxel analysis supported seed-seed findings in SUS mice but also indicated significantly decreased FC between the vDG and anterior cingulate area compared to CON mice. Interestingly, RES mice exhibited enhanced FC between the vDG and anterior cingulate area compared to SUS mice. Moreover, RES mice showed greater FC between the infralimbic prefrontal cortex and the nucleus accumbens shell compared to CON mice. These findings indicate unique differences in FC patterns in phenotypically distinct SUS and RES mice that could represent a neurobiological basis for depression, anxiety, and negative-coping behaviors that are associated with exposure to chronic stress.
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