Cognitive Neuroimaging Lab

Cerebral infarction is a frequent and serious complication of tuberculous meningitis (TBM), contributing substantially to morbidity and mortality. Moreover, studies on infarct patterns and associated factors/predictors remain limited in TBM. ACT-TBM trial (Aspirin or Clopidogrel Therapy in the Treatment of Tuberculous Meningitis) evaluated the efficacy and safety of adjunctive antiplatelet therapy (aspirin or clopidogrel) to standard antitubercular therapy in TBM for the occurrence of stroke or cerebral infarction. Here, we conducted a secondary analysis of the ACT-TBM trial to characterize the patterns, associated factors, and predictors of cerebral infarction in TBM. We utilized data of 237 patients from the ACT-TBM randomized controlled trial conducted at 2 tertiary centers in India (2019-2023). Serial magnetic resonance imaging and magnetic resonance angiography were performed at baseline, 1 month, and 3 months in the primary trial. Cerebral infarctions were categorized by size, vascular territory, and number. Multivariable logistic regression models were performed using variables with P<0.1 on univariable analysis and clinical relevance. Model estimates are reported as adjusted odds ratios (aORs) with 95% CIs. Of the 237 patients enrolled, 226 were included after excluding 11 with missing imaging or incomplete follow-up data. Among these, 84 (37%) had cerebral infarction. Median age of the entire cohort was 26 years (interquartile range, 20-36), and 134 (59.29%) were females. Multiple infarcts were observed in 66 (78.6%) patients. Most frequent location of infarction was in the basal ganglia (n=61, 72.6%), subcortical white matter (n=44, 52.4%), and cortex (n=32, 38.1%). Arterial occlusion occurred in 49 (61.25%) patients with cerebral infarction versus 35 (25.55%) without (P<0.001). In multivariable adjusted models, Grade 3 TBM (aOR, 3.94 [95% CI, 1.19-13.08]; P=0.025), and arterial occlusion (aOR, 4.43 [95% CI, 2.19-8.96]; P<0.001) were associated with infarction. Among those with infarction, 27 (32.14%) patients (13.17% of the available cohort) developed new infarctions on follow-up. Modified antitubercular therapy (antitubercular therapy; aOR, 3.10 [95% CI, 1.18-8.09]; P=0.021) and arterial occlusion (aOR, 4.23 [95% CI, 1.40-12.75]; P=0.01) significantly predicted new infarctions. Presence of exudates was associated with arterial occlusion (aOR, 2.86 [95% CI, 1.08-7.56]; P=0.034). Cerebral infarction is common in TBM and associated with disease severity and arterial occlusion. Modified antitubercular therapy predicted new infarcts, while basal exudates were associated with vascular occlusion, highlighting the need for vigilant monitoring and optimized therapeutic strategies.

Abstract How does the human brain represent the meaning of abstract symbols? Some theories postulate the existence of semantic spaces where concepts occupy positions that reflect their conceptual relationships. In the number domain, psychological evidence suggests that integers are represented along a mental number line which, with education, integrates higher-level number concepts such as fractions. To test this hypothesis, we recorded whole-brain 7T fMRI responses to integer and fraction symbols during a magnitude comparison task. Consistent with predictions, we found both behavioral and neural numerical distance effects. Activation vectors in intraparietal, inferior temporal, prefrontal, hippocampal, and parahippocampal cortices formed a two-dimensional semantic space organized by numerical magnitude and domain (fractions versus integers). Gaussian fits revealed a topographic map of numerical preferences in the anterior inferior parietal cortex, common to both domains. Our results suggest that, in educated adults, a joint semantic map integrates fractions and integers and supports symbolic magnitude representation and comparison.

Evaluation of data quality always remains labor-intensive, but it is a critical prerequisite in clinical neuroscience research to ensure the reliability and reproducibility of research outcomes. The unavailability of quality control (QC) pipelines on a single platform for multimodal neuroimaging data has been a limiting factor in extensive brain research. Moreover, existing QC pipelines primarily involve MRI-based data only. In this context, we developed a neuroimaging and neurospectroscopy (NINS) QC platform "NINS_QC" for multimodal neuroimaging (MRI, fMRI, DTI, MRS) and neuropsychological data. NINS_QC is a transparent and user-friendly framework that can be distributed as a standalone or in the form of a downloadable executable file. This allows local processing of multimodal neuroimaging data and is well-suited for environments with stringent data privacy requirements. The presence of metrics-based analysis, along with visual-based assessment, has added one more attribute to this platform.

Abstract Normative Modelling (‘brain growth charting’) is now a well-established method for computational psychiatry and involves charting centiles of variation across a population in terms of mappings between biology and behavior, providing statistical inferences at the level of the individual. These models have helped the field to move away from case-control analysis toward individual-level analysis. Correspondingly, normative modelling has now been applied to chart brain development and ageing in many populations and has been used to quantify individual deviations across various neurological and psychiatric conditions. This has been supported by large-scale models that are openly accessible for diverse brain imaging modalities. As normative modelling continues to grow, several recent methodological developments, such as non-Gaussian models, longitudinal models, and federated learning, have been implemented in different software tools, including the Predictive Clinical Neuroscience toolkit (PCNtoolkit). In this protocol update, we provide: (i) a revised overview of this methodological landscape; (ii) an update to our 2022 standardised analytical protocol for normative modelling of neuroimaging data, including options for federated and longitudinal normative models; (iii) practical guidance suited to both novice and experienced practitioners supported by open-source code examples implemented in the refactored version of PCNtoolkit; and (iv) updated models for cortical thickness, volumetric data, diffusion-weighted imaging and longitudinal data for use by the community.

Repetitive head impacts (RHIs) in soccer have been associated with long-term risk for neurodegenerative disease. The pathophysiology is largely unknown. This study aims to investigate alterations in cerebral blood flow (CBF) in athletes exposed to RHI compared with athlete controls (CTL). Given that females are known to exhibit higher CBF than males, we also explore sex-specific differences. Finally, we investigate the relationship between CBF and neuropsychological functioning and RHI measures. This study includes 82 amateur athletes (mean age 22.8 ± 1.6 years; 48.9% female). Participants underwent 3T magnetic resonance imaging and completed neuropsychological testing, including questionnaires on stress, resilience, and sleep quality, and computerized assessment of executive function, memory, and processing speed. CBF was assessed using MR pulsed arterial spin labeling. Analysis of covariance was applied to assess the effect of RHI exposure and sex on CBF. Associations between CBF and neuropsychological functioning were analyzed using linear regression models. The analysis was conducted hierarchically, beginning with global gray matter CBF (level 1), followed by cortical and deep gray matter (level 2), and finally brain lobes (level 3). Correction for multiple comparisons was applied at each hierarchical level using false discovery rate, with a significance threshold set at p < 0.05 after correction. We found higher CBF in athletes with RHI exposure (RHI group) compared with athletes with <5 years of exposure to RHI (CTL group; Δ 95% confidence interval [95% CI] = 3.9 [0.5, 7.3] mL/100g/min, p = 0.027). Female athletes with RHI exposure exhibited higher CBF in the global gray matter compared with female CTL (Δ[95% CI] = 6.6 [1.6, 11.5] mL/100g/min, p = 0.013). Among males, individuals with RHI exposure demonstrated higher CBF in the occipital lobe compared with male CTL (Δ[95% CI] = 4.9 [0.3, 9.5] mL/100g/min, p = 0.047). There were no statistically significant associations between CBF and neuropsychological functioning. In the RHI group, years of soccer play were positively associated with whole-brain CBF. Results from this study suggest an association between RHI exposure and higher CBF, a measure of brain activity. Furthermore, we report sex-specific patterns of higher CBF in individuals exposed to RHI, with more widespread elevated CBF in women and more localized higher CBF in men. While these findings highlight the importance of investigating sex-specific effects, there were no associations between CBF and neuropsychological functioning. Future studies are warranted to determine the clinical relevance of the observed sex-specific effects to RHI.

ABSTRACTWe previously performed data‐driven classification based on large‐scale neuroimaging datasets to identify a potential novel subtype of psychiatric disorders characterized by the pronounced enlarged ventricles and cognitive impairment (EVCI) in one cohort. However, these findings have yet to be validated across independent cohorts. Herein, we investigated the availability of cognitive data in other cohorts in the Cognitive Genetics Collaborative Research Organization (COCORO) project to assess the presence of cognitive impairment (CI). Subsequently, we compared the prevalence of CI, lateral ventricular volume, and demographic and clinical characteristics between individuals with EVCI and those with EV but without CI (EV‐nonCI). Cognitive data were available for 27 individuals with EVCI from the other 8 cohorts. Among the 27 individuals in the EV group, 5 (18.5%) exhibited EVCI, all of whom were diagnosed with schizophrenia. Three individuals (60%) in the EVCI group were male. Compared with the EV‐non‐CI group, the EVCI group presented significantly lower current intellectual quotient (IQ) scores (p = 5.8 × 10−4), but there was no significant difference in premorbid IQ (p = 0.56). Furthermore, there was no significant difference in the Z scores for lateral ventricular volume between the groups (p = 0.26). This study validated the existence of the EVCI subgroup in independent cohorts, thus supporting our previous findings.

Cannabis exposure has increased alongside broader legalization, yet its potential relationship to hair follicle biology and diffuse shedding patterns remains incompletely characterized. This study aimed to examine whether simulated cannabis exposure levels are associated with markers of diffuse hair shedding using a Monte Carlo-generated cohort.A synthetic dataset of 140 subjects was generated using a Monte Carlo framework parameterized by published trichoscopic follicular density values, nationally reported cannabis exposure distributions, and psychometric properties of validated hair loss and dermatology-related quality-of-life instruments. Cannabis exposure, follicular density, and Self-Assessment of Hair Loss (SAHL) scores were sampled from probability distributions informed by published means, standard deviations, and hypothetical covariance structures rather than from individual-level patient data. The simulated dataset was analyzed using Pearson correlations, linear regression, and analysis of covariance (ANCOVA) with demographic covariates.Higher cannabis exposure was associated with increased SAHL severity (r = 0.31, p < 0.01) and reduced follicular density (r = -0.38, p < 0.05). Both associations remained statistically significant after covariate adjustment, with larger effect magnitudes observed in female-assigned profiles.As these modeled relationships are non-causal and arise from simulated rather than clinical data, they serve only as hypothesis-generating signals. However, this exposure-level simulation demonstrates that publicly available epidemiologic and trichoscopic data can be integrated through computational modeling to generate falsifiable hypotheses regarding cannabis exposure and diffuse shedding phenotypes. Future studies should incorporate objective biological mediators, longitudinal imaging, and clinical datasets to determine whether these modeled associations correspond to measurable biological effects.

The macaque genus includes 25 species with diverse social systems, ranging from low to high social tolerance grades. Such interspecific behavioral variability provides a unique model to tackle the evolutionary foundation of primate social brain. Yet, the neuroanatomical correlates of these social tolerance grades remain unknown. To address this question, we expressed social tolerance grades within a novel cognitive framework and analyzed post-mortem structural scans from 12 macaque species. Our results show that amygdala volume is a subcortical predictor of macaques’ social tolerance, with high tolerance species exhibiting larger amygdala than low tolerance ones. We further investigated the developmental trajectory of amygdala across social grades and found that intolerant species showed a gradual increase in relative amygdala volume across the lifespan. Unexpectedly, tolerant species exhibited a decrease in relative amygdala volume across the lifespan, contrasting with the age-related increase observed in intolerant species—a developmental pattern previously undescribed in primates. Taken together, these findings provide valuable insights into the cognitive, neuroanatomical and evolutionary basis of primates’ social behaviors.

The perception and production of regular geometric shapes, a characteristic trait of human cultures since prehistory, has unknown neural mechanisms. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the brain systems underlying this ability, as well as their dynamics, we collected functional MRI in both adults and 6-year-olds, and magnetoencephalography data in adults, during the perception of simple shapes such as hexagons, triangles, and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by bigger deep-learning models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.

The perception and production of regular geometric shapes, a characteristic trait of human cultures since prehistory, has unknown neural mechanisms. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the brain systems underlying this ability, as well as their dynamics, we collected functional MRI in both adults and 6-year-olds, and magnetoencephalography data in adults, during the perception of simple shapes such as hexagons, triangles, and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by bigger deep-learning models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.