Prefrontal and posterior parietal contributions to the perceptual awareness of touch

Sensory and motor cortices parcellations estimated via distance-weighted sparse representation with application to autism spectrum disorder

BACKGROUND

Motor impairments and sensory processing abnormalities are prevalent in autism spectrum disorder (ASD), closely related to the core functions of the primary motor cortex (M1) and the primary somatosensory cortex (S1). Currently, there is limited knowledge about potential therapeutic targets in the subregions of M1 and S1 in ASD patients. This study aims to map clinically significant functional subregions of M1 and S1.

METHODS

Resting-state functional magnetic resonance imaging data (NTD = 266) from Autism Brain Imaging Data Exchange (ABIDE) were used for subregion modeling. We proposed a distance-weighted sparse representation algorithm to construct brain functional networks. Functional subregions of M1 and S1 were identified through consensus clustering at the group level. Differences in the characteristics of functional subregions were analyzed, along with their correlation with clinical scores.

RESULTS

We observed symmetrical and continuous subregion organization from dorsal to ventral aspects in M1 and S1, with M1 subregions conforming to the functional pattern of the motor homunculus. Significant intergroup differences and clinical correlations were found in the dorsal and ventral aspects of M1 (p < 0.05/3, Bonferroni correction) and the ventromedial BA3 of S1 (p < 0.05/5). These functional characteristics were positively correlated with autism severity. All subregions showed significant results in the ROI-to-ROI intergroup differential analysis (p < 0.05/80).

LIMITATIONS

The generalizability of the segmentation model requires further evaluation.

CONCLUSIONS

This study highlights the significance of M1 and S1 in ASD treatment and may provide new insights into brain parcellation and the identification of therapeutic targets for ASD.

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75-96.9% of channels) with a 1.65-5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2-15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain-computer interface (BCI).

Analogous cognitive strategies for tactile learning in the rodent and human brain

Evolution has molded individual species' sensory capacities and abilities. In rodents, who mostly inhabit dark tunnels and burrows, the whisker-based somatosensory system has developed as the dominant sensory modality, essential for environmental exploration and spatial navigation. In contrast, humans rely more on visual and auditory inputs when collecting information from their surrounding sensory space in everyday life. As a result of such species-specific differences in sensory dominance, cognitive relevance and capacities, the evidence for analogous sensory-cognitive mechanisms across species remains sparse. However, recent research in rodents and humans yielded surprisingly comparable processing rules for detecting tactile stimuli, integrating touch information into percepts, and goal-directed rule learning. Here, we review how the brain, across species, harnesses such processing rules to establish decision-making during tactile learning, following canonical circuits from the thalamus and the primary somatosensory cortex up to the frontal cortex. We discuss concordances between empirical and computational evidence from micro- and mesoscopic circuit studies in rodents to findings from macroscopic imaging in humans. Furthermore, we discuss the relevance and challenges for future cross-species research in addressing mutual context-dependent evaluation processes underpinning perceptual learning.

Altered regional brain activity and functional connectivity patterns in major depressive disorder: A function of childhood trauma or diagnosis?

Childhood trauma (CT) is a non-specific risk factor for major depressive disorder (MDD). However, the neurobiological mechanisms of MDD with CT remain unclear. In the present study, we sought to determine the specific brain regions associated with CT and MDD etiology. Fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) analyses were performed to assess alterations of intrinsic brain activity in MDD with CT, MDD without CT, healthy controls with CT, and healthy controls without CT. Two-by-two factorial analyses were performed to examine the effects of the factors "MDD" and "CT" on fALFF and FC. Moderator analysis was used to explore whether the severity of depression moderated the relationship between CT and aberrant fALFF. We found that the etiological effects of MDD and CT exhibited negative impacts on brain dysfunction including altered fALFF in the left postcentral gyrus, left lingual gyrus, left paracentral lobule (PCL), and left cuneus. Decreased FC was observed in the following regions: (i) the left lingual gyrus seed and the left fusiform gyrus as well as the right calcarine cortex; (ii) the left PCL seed and the left supplementary motor area, left calcarine cortex, left precentral gyrus, and right cuneus; (iii) the left postcentral gyrus seed and left superior parietal lobule, right postcentral gyrus, and left precentral gyrus. Furthermore, the severity of depression acted as a moderator in the relationship between CT and aberrant fALFF in the left PCL. These data indicate that MDD patients with and without trauma exposure are clinically and neurobiologically distinct.

Reduced frontal white matter microstructure in healthy older adults with low tactile recognition performance

The aging of the nervous system is a heterogeneous process. It remains a significant challenge to identify relevant markers of pathological and healthy brain aging. A central aspect of aging are decreased sensory acuities, especially because they correlate with the decline in higher cognitive functioning. Sensory and higher cognitive processing relies on information flow between distant brain areas. Aging leads to disintegration of the underlying white matter tracts. While this disintegration is assumed to contribute to higher cognitive decline, data linking structural integrity and sensory function are sparse. The investigation of their interrelation may provide valuable insight into the mechanisms of brain aging. We used a combined behavioral and neuroimaging approach and investigated to what extent changes in microstructural white matter integrity reflect performance declines in tactile pattern recognition with aging. Poor performance in older participants was related to decreased integrity in the anterior corpus callosum. Probabilistic tractography showed that this structure is connected to the prefrontal cortices. Our data point to decreased integrity in the anterior corpus callosum as a marker for advanced brain aging. The correlation between impaired tactile recognition and disintegration in frontal brain networks could provide an explanation why the decrease of sensory function predicts cognitive decline.

Encoding schemes in somatosensation: From micro- to meta-topography

Encoding schemes are systematic large-scale arrangements that convert incoming sensory information into a format required for further information processing. The increased spatial resolution of brain images obtained with ultra-high field magnetic resonance imaging at 7 T (7T-MRI) and above increases the granularity and precision of processing units that mediate the link between neuronal encoding and functional readouts. Here, these new developments are reviewed with a focus on human tactile encoding schemes derived from small-scale processing units (in the order of 0.5-5 mm) that are relevant for theoretical and practical concepts of somatosensory encoding and cortical plasticity. Precisely, we review recent approaches to characterize meso-scale maps, layer units, and cortical fields in the sensorimotor cortex of the living human brain and discuss their impact on theories of perception, motor control, topographic encoding, and cortical plasticity. Finally, we discuss concepts on the integration of small-scale processing units into functional networks that span multiple topographic maps and multiple cortical areas. Novel research areas are highlighted that may help to bridge the gap between cortical microstructure and meta-topographic models on brain anatomy and function.