Neuroanatomical dissociation for taxonomic and thematic knowledge in the human brain.
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ABSTRACT:
SUBMITTER: Schwartz MF
PROVIDER: S-EPMC3100928 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSchizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated.MethodsA total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively.ResultsPatients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen's Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western).ConclusionsThese findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.
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Project description:Current models and theories of semantic knowledge primarily capture taxonomic relationships (DOG and WOLF) and largely do not address the role of thematic relationships in semantic knowledge (DOG and LEASH). Recent evidence suggests that processing or representation of thematic relationships may be distinct from taxonomic relationships. If taxonomic and thematic relations are distinct, then there should be a cost associated with switching between them even when the task remains constant. This hypothesis was tested using two different semantic-relatedness judgment tasks: Experiment 1 used a triads task and Experiment 2 used an oddball task. In both experiments, participants were faster to respond when the same relationship appeared on consecutive trials than when the relationship types were different, even though the task remained the same and the specific relations were different on each trial. These results are consistent with the theory that taxonomic and thematic relations rely on distinct processes or representations.
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Project description:Inductive reasoning can be performed in different contexts, but it is unclear whether the neural mechanism of reasoning performed in a thematic context (e.g., bee has x, so honey has x) is the same as that performed in a taxonomic context (e.g., bee has x, so butterfly has x). In the present study, participants were required to judge whether a conclusion was acceptable or not based on its premise, for which the taxonomic or thematic distances between premise and conclusion objects were either far or near. The Event related potential (ERP) results indicated that the effect of context (taxonomic vs. thematic) was initially observed in the P2 component; while the distance effect (far vs. near) was observed in N400 and late components. Moreover, the distance effect on thematic-based inductive reasoning was found in the anterior regions, while the distance effect on taxonomic-based inductive reasoning conditions was found in the posterior regions. These results support the view that inductive reasoning is performed differently under different semantic contexts.
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Project description:Repeated processing of the same information is associated with decreased neuronal responses, termed repetition suppression (RS). Although RS effects (i.e., the difference in activity between novel and repeated stimuli) have been demonstrated within several brain regions, such as the medial temporal lobe, their precise neural mechanisms still remain unclear. Here, we used functional magnetic resonance imaging together with psychopharmacology in 48 healthy human subjects, demonstrating that RS effects within the mesolimbic system are differentially modulated by cholinergic and dopaminergic stimulation. The dopamine precursor levodopa (100 mg) attenuated RS within the hippocampus, parahippocampal cortex, and substantia nigra/ventral tegmental area, and the degree of this reduction correlated with recognition memory performance 24 h later. The acetylcholinesterase inhibitor galantamine (8 mg), in contrast, reversed RS into repetition enhancement, showing no relationship to subsequent recognition memory. This suggests that novelty sensitive neural populations of the mesolimbic system can dynamically shift their responses depending on the balance of cholinergic and dopaminergic neurotransmission, and these shifts can influence memory retention.
| S-EPMC3977623 | biostudies-literature
Project description:Allocation of attentional resources to portions of the available sensory input can be regulated by bottom-up processes, i.e., spontaneous orientation towards an oncoming stimulus (stimulus-driven attention), and by top-down processes, i.e., intentionally and driven by knowledge, expectation and goals. The present study aimed at advancing the understanding of brain networks mediating bottom-up and top-down control of visuospatial attention by employing a paradigm that parametrically varied demands on these two processes. Spatial predictability of peripheral targets was parametrically varied by centrally cueing one, two, three or four of four possible locations. Reaction time decreased linearly with more precise valid cueing of the target location and increased with more precise invalid cueing. Event-related functional magnetic resonance imaging (fMRI) enabled measurement of blood oxygenation level-dependent (BOLD) responses to cues and to targets. A mostly left-hemispheric network consisting of left intraparietal sulcus, inferior and superior parietal lobule, bilateral precuneus, middle frontal gyri including superior frontal sulci, and middle occipital gyri displayed BOLD responses to cues that increased linearly with more precise spatial cueing, indicating engagement by top-down spatial selective attention. In contrast, bilateral temporoparietal junction, cingulate gyrus, right precentral gyrus and anterior and posterior insula, bilateral fusiform gyri, lingual gyri and cuneus displayed BOLD responses to targets that increased with their spatial unpredictability, indicating engagement by stimulus-driven orienting. The results suggest two largely dissociated neural networks mediating top-down and bottom-up control of visuospatial selective attention.
| S-EPMC2652125 | biostudies-literature
Project description:Semantic cognition includes taxonomic and thematic relationships, as well as control systems to retrieve and manipulate semantic knowledge to suit specific tasks or contexts. A recent report (Thompson et al., 2017) suggested that retrieving thematic relationships (i.e., relations based on participation in the same event or scenarios) requires more effort or cognitive control, especially when the relevant relations are weak, than retrieving identity relations that are based on sensory-motor features. It is not clear whether the same contrast applies to the broader set of taxonomic relations, which are also based on shared sensory-motor features. In this study we tested cognitive control requirements of retrieving taxonomic and thematic knowledge using a physiological measure of cognitive effort: pupil dilation. Participants completed a semantic relatedness judgement task that manipulated semantic type (thematic vs. taxonomic) and relatedness strength (high vs. low) of word pairs. Cognitive control in the similarity task was examined using task-evoked pupillary responses (TEPRs), as well as standard behavioral measures (reaction times and accuracy). Compared with high-strength relations, low-strength semantic relations elicited larger TERPs, slower reaction times, and lower accuracy, consistent with higher control demands. Compared to thematic relations, taxonomic relations also elicited larger TERPs and slower reaction times, suggesting that retrieving taxonomic relations required more cognitive effort. Critically, our pupillometric data indicated that controlled processing was particularly important for low-strength taxonomic pairs rather than low-strength thematic pairs. These findings indicate that semantic control demands are primarily determined by relatedness strength, not whether the relationship is taxonomic or thematic.
| S-EPMC6634386 | biostudies-literature
Project description:We report the results of a study comparing the temporal dynamics of thematic and taxonomic knowledge activation in a picture-word priming paradigm using event-related potentials. Although we found no behavioral differences between thematic and taxonomic processing, ERP data revealed distinct patterns of N400 and P600 amplitude modulation for thematic and taxonomic priming. Thematically related target stimuli elicited less negativity than taxonomic targets between 280-460 ms after stimulus onset, suggesting easier semantic processing of thematic than taxonomic relationships. Moreover, P600 mean amplitude was significantly increased for taxonomic targets between 520-600 ms, consistent with a greater need for stimulus reevaluation in that condition. These results offer novel evidence in favor of a dissociation between thematic and taxonomic thinking in the early phases of conceptual evaluation.
| S-EPMC5728532 | biostudies-literature
Project description:Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided discrimination of patients from control subjects. Unsupervised, data-driven clustering of structural MRI (sMRI) data was used to identify 2 subtypes of schizophrenia patients drawn from a US-based open science repository (n = 71) and we quantified classification improvements compared to controls (n = 74) using supervised machine learning. We externally validated the unsupervised and supervised learning models in a heterogeneous German validation sample (n = 316), and characterized symptom, cognition, and longitudinal symptom change signatures. Stratification improved classification accuracies from 68.5% to 73% (subgroup 1) and 78.8% (subgroup 2), respectively. Increased accuracy was also found when models were externally validated, and an average gain of 9% was found in supplementary analyses. The first subgroup was associated with cortical and subcortical volume reductions coupled with substantially longer illness duration, whereas the second subgroup was mainly characterized by cortical reductions, reduced illness duration, and comparatively less negative symptoms. Individuals within each subgroup could be identified using just 10 clinical questions at an accuracy of 81.2%, and differential cognitive and symptom course signatures were suggested in multivariate analyses. Our findings suggest that sMRI-based subtyping enhances the neuroanatomical discrimination of schizophrenia by identifying generalizable brain patterns that align with a clinical staging model of the disorder. These findings could be used to improve illness stratification for biomarker-based computer-aided diagnoses.
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Project description:PurposeDevelopment of a freely available stroke population-specific anatomical CT/MRI atlas with a reliable normalisation pipeline for clinical CT.MethodsBy reviewing CT scans in suspected stroke patients and filtering the AIBL MRI database, respectively, we collected 50 normal-for-age CT and MRI scans to build a standard-resolution CT template and a high-resolution MRI template. The latter was manually segmented into anatomical brain regions. We then developed and validated a MRI to CT registration pipeline to align the MRI atlas onto the CT template. Finally, we developed a CT-to-CT-normalisation pipeline and tested its reliability by calculating Dice coefficient (Dice) and Average Hausdorff Distance (AHD) for predefined areas in 100 CT scans from ischaemic stroke patients.ResultsThe resulting CT/MRI templates were age and sex matched to a general stroke population (median age 71.9 years (62.1-80.2), 60% male). Specifically, this accounts for relevant structural changes related to aging, which may affect registration. Applying the validated MRI to CT alignment (Dice > 0.78, Average Hausdorff Distance < 0.59 mm) resulted in our final CT-MRI atlas. The atlas has 52 manually segmented regions and covers the whole brain. The alignment of four cortical and subcortical brain regions with our CT-normalisation pipeline was reliable for small/medium/large infarct lesions (Dice coefficient > 0.5).ConclusionThe newly created CT-MRI brain atlas has the potential to standardise stroke lesion segmentation. Together with the automated normalisation pipeline, it allows analysis of existing and new datasets to improve prediction tools for stroke patients (free download at https://forms.office.com/r/v4t3sWfbKs ).
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Project description:We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.
| S-EPMC7175006 | biostudies-literature