Connectivity of the ventral visual cortex is necessary for object recognition in patients

Mechanisms underlying category learning in the human ventral occipito-temporal cortex

The human ventral occipito-temporal cortex (VOTC) has evolved into specialized regions that process specific categories, such as words, tools, and animals. The formation of these areas is driven by bottom-up visual and top-down nonvisual experiences. However, the specific mechanisms through which top-down nonvisual experiences modulate category-specific regions in the VOTC are still unknown. To address this question, we conducted a study in which participants were trained for approximately 13 h to associate three sets of novel meaningless figures with different top-down nonvisual features: the wordlike category with word features, the non-wordlike category with nonword features, and the visual familiarity condition with no nonvisual features. Pre- and post-training functional MRI (fMRI) experiments were used to measure brain activity during stimulus presentation. Our results revealed that training induced a categorical preference for the two training categories within the VOTC. Moreover, the locations of two training category-specific regions exhibited a notable overlap. Remarkably, within the overlapping category-specific region, training resulted in a dissociation in activation intensity and pattern between the two training categories. These findings provide important insights into how different nonvisual categorical information is encoded in the human VOTC.

Inter-individual differences in baseline dynamic functional connectivity are linked to cognitive aftereffects of tDCS

Transcranial direct current stimulation (tDCS) has the potential to modulate cognitive training in healthy aging; however, results from various studies have been inconsistent. We hypothesized that inter-individual differences in baseline brain state may contribute to the varied results. We aimed to explore whether baseline resting-state dynamic functional connectivity (rs-dFC) and/or conventional resting-state static functional connectivity (rs-sFC) may be related to the magnitude of cognitive aftereffects of tDCS. To achieve this aim, we used data from our double-blind randomized sham-controlled cross-over tDCS trial in 25 healthy seniors in which bifrontal tDCS combined with cognitive training had induced significant behavioral aftereffects. We performed a backward regression analysis including rs-sFC/rs-dFC measures to explain the variability in the magnitude of tDCS-induced improvements in visual object-matching task (VOMT) accuracy. Rs-dFC analysis revealed four rs-dFC states. The occurrence rate of a rs-dFC state 4, characterized by a high correlation between the left fronto-parietal control network and the language network, was significantly associated with tDCS-induced VOMT accuracy changes. The rs-sFC measure was not significantly associated with the cognitive outcome. We show that flexibility of the brain state representing readiness for top-down control of object identification implicated in the studied task is linked to the tDCS-enhanced task accuracy.

Multi-level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis

Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagnosis, and the analysis of functional connectivity (FC) has become the primary method. However, previous studies mainly focus on brain disease classification based on the low-order FC features, ignoring the potential role of high-order functional relationships among brain regions. To solve this problem, this study proposed a novel multi-level FC fusion classification framework (MFC) for brain disease diagnosis. We firstly designed a deep neural network (DNN) model to extract and learn abstract feature representations for the constructed low-order and high-order FC patterns. Both unsupervised and supervised learning steps were performed during the DNN model training, and the prototype learning was introduced in the supervised fine-tuning to improve the intra-class compactness and inter-class separability of the feature representation. Then, we combined the learned multi-level abstract FC features and trained an ensemble classifier with a hierarchical stacking learning strategy for the brain disease classification. Systematic experiments were conducted on two real large-scale fMRI datasets. Results showed that the proposed MFC model obtained robust classification performance for different preprocessing pipelines, different brain parcellations, and different cross-validation schemes, suggesting the effectiveness and generality of the proposed MFC model. Overall, this study provides a promising solution to combine the informative low-order and high-order FC patterns to further promote the classification of brain diseases.

Lesion topography of posterior cerebral artery infarcts

This study analyzed the topography of acute ischemic stroke in the posterior cerebral artery (PCA) territory. We studied 84 patients with unilateral ischemic PCA stroke. Patients were classified according to lesion levels as cortico-subcortical (superficial), combined (cortical and mesodiencephalic) or isolated thalamic. To receive a lesion map, data from acute MR and CT imaging were normalized and labelled automatically by mapping to stereotaxic anatomical atlases. Cortical lesions accounted for 41.7%, combined for 36.9%, and isolated thalamic lesions for 21.4%. The maximum overlay of ischemia and, thus, highest occurrence of PCA ischemic stroke was found in the ventral and medial occipito-temporal cortex and adjacent white matter association tracts. Dorsal and peripheral segments of the occipito-temporo-parietal region were only rarely lesioned. This configuration was similar in both hemispheres. Consistent with this lesion pattern, visual field defects (VFD) were the most frequent signs, followed by sensorimotor signs, dizziness and sopor, cognitive and oculomotor deficits, and ataxia. The three vascular subgroups differed not only by their anatomical lesion profile and lesion load, but also by their clinical manifestation; although patients with combined and thalamic lesions were sigificantly younger, they were more disabled than participants with cortical lesions. VFD were only found in cortical and combined, and oculomotor deficits only in mesodiencephalic lesions. White matter lesions were common in the cortico-subcortical and the combined group. Basal occipito-temporal and calcarine regions, and neighbouring white matter tracts have the highest risk of ischemia in acute PCA stroke.

Connectivity of the ventral visual cortex is necessary for object recognition in patients

The functional profiles of regions in the ventral occipital-temporal cortex (VTC), a critical region for object visual recognition, are associated with the VTC connectivity patterns to nonvisual regions relevant to the corresponding object domain. However, whether and how whole-brain connections affect recognition behavior remains untested. We directly examined the necessity of VTC connectivity in object recognition behavior by testing 82 patients whose lesion spared relevant VTC regions but affected various white matter (WM) tracts and other regions. In these patients, we extracted the whole-brain anatomical connections of two VTC domain-selective (large manmade objects and animals) clusters with probabilistic tractography, and examined whether such connectivity pattern can predict recognition performance of the corresponding domains with support vector regression (SVR) analysis. We found that the whole-brain anatomical connectivity of large manmade object-specific cluster successfully predicted patients' large object recognition performance but not animal recognition or control tasks, even after we excluded connections with early visual regions. The contributing connections to large object recognition included tracts between VTC-large object cluster and distributed regions both within and beyond the visual cortex (e.g., putamen, superior, and middle temporal gyrus). These results provide causal evidence that the VTC whole-brain anatomical connectivity is necessary for at least certain domains of object recognition behavior.