Biology subjects

Saggar, M.

Publications and source records attributed to Saggar, M..

22 records · Page 2Linked to original sources

bioRxiv2021

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking

Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often indirectly implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also including two of the widely used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using data-driven eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode networks, but also by a higher inter-network coupling between the two. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and data-driven approach, we provide direct empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

neuroscience↗

bioRxiv2020

Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

Whereas brain imaging tools like functional Magnetic Resonance Imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the datas apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, i.e, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective considers the condition-dependency of the brains Functional Connectivity (FC), i.e., the similarity matrix computed across the variables of a multivariate signal. More recently, metric spaces that think of the data topologically, e.g., as an abstract geometric object, have become available. In the abstract, uncertainty prevails regarding the distortions imposed by the mode of measurement upon the object under study. Features that are invariant under continuous deformations, such as rotation and inflation, constitute the features of topological data analysis. While there are strengths and weaknesses of each metric space, we find that metric spaces that track topological features are optimal descriptors of the brains experimentally defined states. AUTHOR SUMMARYTime-Varying Functional Connectivity (TVFC) leverages brain imaging data to interpret brain function as time-varying patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brains functional self-organization. One tool from TDA, "persistent homology", observes the occurrence and persistence of n-dimensional holes in a sequence of simplicial complexes extracted from a weighted graph. The occurrence of such holes within the TVFC graph may indicate preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional metrics at the task of segmenting brain states that differ across experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment high versus low performance levels under common stimuli, and generalize better across volunteers. These findings support the topological interpretation of brain dynamics.

neuroscience↗

bioRxiv2020

Complexity of resting brain dynamics shaped by multiscale structural constraints

The brain is a complex system exhibiting ever-evolving activity patterns without any external inputs or tasks. Such intrinsic dynamics (or lack thereof) are thought to play crucial roles in typical as well as atypical cognitive functioning. Linking the ever-changing intrinsic dynamics to the rather static anatomy is a challenging endeavor. Dynamical systems models are important tools for understanding how structure and function are linked in the brain. Here, we provide a novel modeling framework to examine such structure-function relations. Our deterministic approach complements previous modeling frameworks, which typically focus on noise-driven (or stochastic) dynamics near a single attractor. We examine the overall organizations of and coordination between all putative attractors. Using our approach, we first provide evidence that examining cross-attractor coordination between brain regions could better predict human functional connectivity than examining noise-driven near-attractor dynamics. Further, we observed that structural connections across scales modulate the energy costs of such cross-attractor coordination. Overall, our work provides a systematic framework for characterizing intrinsic brain dynamics as a web of cross-attractor transitions and associated energy costs. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.

neuroscience↗

bioRxiv2019

Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm

Humans have an extraordinary ability to interact and cooperate with others, which plays a pivotal role in societies at large. Despite its potential social and evolutionary significance, research on finding the neural correlates of collaboration has been limited partly due to restrictions on simultaneous neuroimaging of more than one participant (a.k.a. hyperscanning). A series of works now exists that used dyadic fMRI hyperscanning to examine the interaction between two participants. However, to our knowledge, no study to date has aimed at revealing the neural correlates of social interactions using a 3-person (or triadic) fMRI hyperscanning paradigm. Here, for the first time, we simultaneously measured the blood-oxygenation-level-dependent (BOLD) signal of triads (m=12 triads; n=36 participants), while they engaged in a joint drawing task based on the social game of Pictionary(R). General linear model (GLM) analysis revealed increased activation in the brain regions previously linked with the theory of mind (ToM) during the collaborative phase compared to the independent phase of the task. Furthermore, using intersubject brain synchronization (IBS) analysis, we revealed increased synchrony of the right temporo-parietal junction (R TPJ) during the collaborative phase. The increased synchrony in the R TPJ was observed to be positively associated with the overall team performance on the task. In sum, our novel paradigm revealed a vital role of the R TPJ among other ToM regions during a triadic collaborative drawing task.

neuroscience↗