Biology subjects

Baths, V.

Publications and source records attributed to Baths, V..

4 recordsLinked to original sources

bioRxiv2026

Altered Baseline Brain Network Topology in High-Risk Individuals Progressing to Mild Cognitive Impairment

BackgroundIdentifying early brain-based markers of cognitive decline is critical for preventive strategies in Alzheimers disease. Individuals with a familial risk may exhibit subtle functional brain changes years before clinical symptoms emerge. This exploratory study examined whether baseline functional brain network topology differentiates high-risk cognitively normal older adults who later progress to mild cognitive impairment (MCI) from those who remain cognitively stable. MethodsBaseline resting-state fMRI data were analyzed from 90 cognitively normal adults with a family history of Alzheimers (PREVENT-AD cohort), classified longitudinally as converters (MCI-C; n=45) or non-converters (MCI-NC; n=45). Whole-brain functional networks were analyzed across multiple thresholds; primary results are reported at 12% network density, with robustness verified at 16% density. Group differences were assessed using ANCOVA or Rank ANCOVA (controlling for age, sex, and education) at an uncorrected threshold (p < 0.05). Predictive utility was evaluated via a 100-repetition nested cross-validation machine-learning framework on a multimodal feature set combining functional network metrics, average cortical thickness, and plasma p-tau217, with covariates included within training folds. ResultsAt baseline, MCI-C participants were older, had fewer years of education, exhibited higher plasma p-tau217 levels, and showed trend-level lower MoCA scores. At 12% density, MCI-C showed increased average nodal strength (F=4.50, p=0.036, p2=0.050) and reduced global efficiency (F=4.07, p=0.046, p2=0.045). Increased betweenness centrality within the Default Mode Network (F=4.07, p=0.046, p2=0.045) and trend-level increases in average clustering (F=3.10, p=0.081, p2=0.035) were observed. Initial largest connected component (LCC) showed a trend-level decrease (F=3.84, p=0.053, p2=0.043). At 16% density, MCI-C exhibited significantly reduced initial LCC (F=4.41, p=0.038, p2=0.049) and increased nodal strength (F=4.29, p=0.041, p2=0.048), with directionally consistent trend-level reductions in global efficiency (F=3.74, p=0.056, p2=0.042). In machine learning, the k-nearest neighbors classifier showed the most stable performance (nested CV accuracy=59.6%; test F1-score=0.56). Feature stability analysis identified global efficiency (selected in 25.8% of iterations) and critical drop (19.4%) as the most consistent predictors. ConclusionBaseline disruptions in functional network integration precede clinical conversion to MCI. The consistent selection of graph-theoretical metrics, particularly global efficiency and critical drop, as top predictors suggests that functional network reorganization provides unique information for classification before widespread cortical atrophy emerges.

neuroscience↗

bioRxiv2023

Feature engineering benchmark for motor relateddecoding through ECoG signals

Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG) signals require efficient recognition of spatiotemporal patterns from a multi-electrodes sensor array. Such signals are excellent candidates for automated pattern recognition through machine learning algorithms. The importance of these patterns can be highlighted through feature extraction techniques. However, the signal variability due to non-stationarity is ignored while extracting features, and which features to use can be challenging to figure out by visual inspection. In this study, we introduce the signal split parameter to account for the variability of the signal and increase the accuracy of the machine learning classifier. We use genetic selection, which allows the selection of the optimal combination of features from a pool of 8 different feature sets. Genetic selection of features increases accuracy and reduces the BCIs prediction time. Along with Genetic selection, we also use a reduced signal length, which leads to a higher Information Transfer Rate. Thus this approach enables the design of a fast and accurate motorrelated EcoG BCI system.

neuroscience↗

bioRxiv2022

Doing what's not wanted: Conflict in incentives and misallocation of behavioural control lead to drug-seeking despite adverse outcomes

Despite being aware of negative consequences and wanting to quit, long-term addicts find it difficult to quit seeking and consuming drugs. This inconsistency between the (often compulsive) behavioural patterns and the explicit knowledge of negative consequences represents a cognitive conflict which is a central characteristic of addiction. Neurobiologically, differential cue-induced activity in distinct striatal subregions, as well as the dopamine connectivity spiraling from ventral striatal regions to the dorsal regions, play critical roles in compulsive drug seeking. The focus of this work is to illustrate the mechanisms that lead to a cognitive conflict and its impact on actions taken i.e. addictive choices. We propose an algorithmic model that captures how the action choices that the agent makes when reinforced with drug-rewards become incongruent with the presence of negative consequences that often follow those choices. We advance the understanding of having a decision hierarchy in representing "cognitive control" and how lack of such control at higher-level in the hierarchy could potentially lead to consolidated drug-seeking habits. We further propose a cost-benefit based arbitration scheme, which mediates the allocation of control across different levels of the decision-making hierarchy. Lastly, we discuss how our work on extending a computational model to an algorithmic one, could in turn also helps us improve the understanding of how drugs hijack the dopamine-spiralling circuit at an implementation level.

neuroscience↗

bioRxiv2021

Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset

Emotion classification using electroencephalography (EEG) data and machine learning techniques has been on the rise in the recent past. However, past studies uses data from medical-grade EEG setup with long set-up time and environment constraints. This paper focuses on classifying emotions on the valence-arousal plane using various feature extraction, feature selection and machine learning techniques. We evaluate different feature extraction and selection techniques and propose the optimal set of features and electrodes for emotion recognition. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The importance of different electrode locations was calculated, which could be used for designing a headset for emotion recognition. The collected dataset and pipeline are also published. Our study achieved a root mean square score (RMSE) of 0.905 on DREAMER, 1.902 on DEAP, and 2.728 on our dataset for valence label and a score of 0.749 on DREAMER, 1.769 on DEAP and 2.3 on our proposed dataset for arousal label respectively.

animal behavior and cognition↗