Biology subjects

Jeyachandra, J.

Publications and source records attributed to Jeyachandra, J..

4 recordsLinked to original sources

bioRxiv2024

Bayesian Optimization of Neurostimulation (BOONStim)

BackgroundTranscranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed. ObjectiveThe current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline. MethodsBOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. BOONStims Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to densely sampled grid optimization. ResultsBayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within 5% error of the maxima detected by grid optimization, and requiring less time. ConclusionsBOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.

neuroscience↗

bioRxiv2022

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after Personalized Intrinsic Network Topography

BackgroundSpatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls using individualized connectivity profiles. MethodsWe utilized resting state and anatomical MRI data from n=406 participants (n = 203 SSD, n = 203 healthy controls) from four cohorts. For each participant, functional timeseries were extracted from 80 cortical regions of interest, representing 6 intrinsic networks using 1) a volume-based approach 2) a surface-based group atlas approach, and 3) Personalized Intrinsic Network Topography (PINT), a personalized surface-based approach (Dickie et al., 2018). Timeseries were also extracted from previously defined intrinsic network subregions of the striatum (Choi et al 2011), thalamus (Ji et al 2019), and cerebellum (Buckner et al 2011). ResultsCompared to a volume-based approach, the correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohens D volume vs surface 0.27-1.00, all p<10^-6) and further increased after PINT (Cohens D surface vs PINT 0.18-0.96, all p <10^-4). In SSD vs HC comparisons, controlling for age, sex, scanner and in-scanner motion, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 357, surface: 562, PINT: 630, FDR corrected). These patterns were found from four different cortical networks - frontal-parietal, sensory-motor, visual, and default mode -- to subcortical regions. ConclusionOur results indicate that individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models (Murray et al 2019). Our results also change our understanding of the specific network-network functional connectivity alterations in people with SSDs, and the extent of those alterations. Future work will examine these new patterns of dysconnectivity with behaviour using dimensional models. Highlights- We evaluated the impact of cortical mapping method (volume-based, surface-based, vs surface personalized: PINT) on resting-state fMRI results in Schizophrenia Spectrum Disorders (SSD). - The use of surface-based approaches and PINT increased the connectivity of cortical networks with the expected subregions of the striatum, thalamus and cerebellum, in comparison to a volume-based approach - whole-brain case-control differences in functional connectivity were more pronounced after surface-based approach and PINT, in comparison to a volume-based approach

neuroscience↗

bioRxiv2019

Sex differences in Variability of Brain Structure Across the Lifespan

Several brain disorders exhibit sex differences in onset, presentation, and prevalence. Increased understanding of the neurobiology of sex-based differences across the lifespan can provide insight into potential disease risk and protective mechanisms. We focused on sex-related differences in variability, which may be indicative of both disease vulnerability and resilience. In n=3,069 participants, from 8-95 years of age, we first analyzed the variance ratio in females vs. males of cortical surface area and global and subcortical volumes for discrete brain regions, and found widespread greater variability in males. In contrast, variance in cortical thickness was similar for males and females. Multivariate analysis that accounts for structural covariance supported variance ratio findings. Findings were present from early life and stable with age. We then examined variability among brain regions by sex. We found significant age-by-sex interactions across neuroimaging metrics, whereby in very early life males had reduced among-region variability compared to females, while in very late life this was reversed. Overall, our findings of greater regional variability but less among-region variability in males in early life may aid our understanding of sex-based risk for neurodevelopmental disorders. In contrast, our findings in late life may provide a potential sex-based risk mechanism for dementia.

neuroscience↗

bioRxiv2019

HD-tDCS over mIPS causally modulates online reach correction

Brain lesion and stimulation studies have suggested posterior parietal cortex and the medial intraparietal sulcus in particular as a crucial hub for online movement error corrections. However, causal evidence for this is still sparse. Indeed, lesion studies are potentially confounded by compensatory reorganization mechanisms while brain stimulation studies have produced heterogeneous results when employing transcranial magnetic stimulation. Here we designed a new complementary paradigm using fMRI-guided high-definition transcranial direct current stimulation (HD-tDCS) of the left medial intraparietal sulcus (mIPS) together with regression-based mediation analysis to re-examine the causal role of mIPS in online reach corrections to jumping targets. We obtained two independent measures of stimulation-induced changes in brain activity by modeling current flow in the brain and through EEG recordings before and after HD-tDCS stimulation. Third, to quantify behavioral effects of HD-tDCS we computed movement curvature as a measure of online correction. We demonstrate that both of our measurements of brain activity were consistent with a polarity-specific modulation of the online correction for targets jumping to the contralateral side of the stimulation. Importantly, using a mediation analysis of the relationship between stimulation current and movement curvature suggests that the induced current modifies brain activity, which then leads to the observed behavioral changes. This unique combination of methods and analysis thus provides complementary evidence for the crucial role of the posterior parietal cortex in online error correction, while at the same time setting a new methodological standard with respect to the causal influence of transcranial direct current stimulation. New & NoteworthyTranscranial direct current stimulation (tDCS) is an interesting and potentially useful tool for asking causal scientific questions and design clinical treatments. With our unique combination of highly accurate fMRI guided stimulation, current forward modeling, EEG recordings before and after the stimulation and behavioral changes we could unravel the causal structure of tDCS. Our approach naturally deals with the variability of tDCS results, increasing its potential usefulness as a tool for research and clinical applications.

neuroscience↗