Studies have indicated that the dorsal attention network (DAN) and the ventral attention network (VAN) functionally interact via several fronto-parietal connector hubs. However, the anatomical connectivity profiles of these connector hubs, and the coupling between the anatomical and functional connectivities of them, are still unknown. In the present study, we found that functional connector hubs anatomically bridged the DAN and VAN based on multimodal magnetic resonance imaging data from the Human Connectome Project (HCP) Consortium and an independent Chinese cohort. The three hubs had unique anatomical connectivity patterns with the attention sub-networks. For each connector hub, the pattern of anatomical connectivity resembled the functional one. Finally, the strength of the anatomical connectivity of these connector hubs was positively associated with the functional connectivity at the group- and individual-levels. Our findings help to better understand the anatomical mechanisms underlying the functional interactions between the DAN and the VAN.

How pain emerges in the human brain remains an unresolved question. Neuroimaging studies have suggested that several brain areas subserve pain perception because their activation correlates with perceived pain intensity. However, painful stimuli are often intense and highly salient; therefore, using both intensity- and saliency-matched control stimuli is crucial to isolate pain-selective brain responses. Here, we used these intensity/saliency-matched painful and non-painful stimuli to test whether pain-selective information can be isolated in the functional magnetic resonance imaging responses elicited by painful stimuli. Using two independent datasets, multivariate pattern analysis was able to isolate features distinguishing the responses triggered by (1) intensity/saliency-matched painful versus non-painful stimuli, and (2) high versus low-intensity/saliency stimuli regardless of whether they elicit pain. This indicates that neural activity in the so-called “pain matrix” is functionally heterogeneous, and part of it carries information related to both painfulness and intensity/saliency. The response features distinguishing these aspects are spatially distributed and cannot be ascribed to specific brain structures.

With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUI-based toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.

How pain emerges from cortical activities remains an unresolved question in pain neuroscience. A first step toward addressing this question consists in identifying brain activities that occur preferentially in response to painful stimuli in comparison to non-painful stimuli. A key confound that has affected this important comparison in many previous studies is the intensity of the stimuli generating painful and non-painful sensations. Here, we compared the brain activity during iso-intense painful and tactile sensations sampled by functional MRI in 51 healthy participants. Specifically, the perceived intensity was recorded for every stimulus and only the stimuli with rigorously matched perceived intensity were selected and compared between painful and tactile conditions. We found that all brain areas activated by painful stimuli were also activated by tactile stimuli, and vice versa. Neural responses in these areas were correlated with the perceived stimulus intensity, regardless of stimulus modality. More importantly, among these activated areas, we further identified a number of brain regions showing stronger responses to painful stimuli than to tactile stimuli when perceived intensity was carefully matched, including the bilateral opercular cortex, the left supplementary motor area and the right frontal middle and inferior areas. Among these areas, the right frontal middle area still responded more strongly to painful stimuli even when painful stimuli were perceived less intense than tactile stimuli, whereas in this condition other regions showed stronger responses to tactile stimuli. In contrast, the left postcentral gyrus, the visual cortex, the right parietal inferior gyrus, the left parietal superior gyrus and the right cerebellum had stronger responses to tactile stimuli than to painful stimuli when perceived intensity was matched. When tactile stimuli were perceived less intense than painful stimuli, the left postcentral gyrus and the right parietal inferior gyrus still responded more strongly to tactile stimuli while other regions now showed similar responses to painful and tactile stimuli. These results suggest that different brain areas may be engaged differentially when processing painful and tactile information, although their neural activities are not exclusively dedicated to encoding information of only one modality but are strongly determined by perceived stimulus intensity regardless of stimulus modality.

Nociceptive and tactile information is processed in the somatosensory system via reciprocal (i.e., feedforward and feedback) projections between the thalamus, the primary (S1) and secondary (S2) somatosensory cortices. The exact hierarchy of nociceptive and tactile information processing within this 'thalamus-S1-S2' network and whether the processing hierarchy differs between the two somatosensory submodalities remains unclear. In particular, two questions related to the ascending and descending pathways have not been addressed. For the ascending pathways, whether tactile or nociceptive information is processed in parallel (i.e., 'thalamus-S1' and 'thalamus-S2') or in serial (i.e., 'thalamus-S1-S2') remains controversial. For the descending pathways, how corticothalamic feedback regulates nociceptive and tactile processing also remains elusive. Here, we aimed to investigate the hierarchical organization for the processing of nociceptive and tactile information in the 'thalamus-S1-S2' network using dynamic causal modeling (DCM) combined with high-temporal-resolution fMRI. We found that, for both nociceptive and tactile information processing, both S1 and S2 received inputs from thalamus, indicating a parallel structure of ascending pathways for nociceptive and tactile information processing. Furthermore, we observed distinct corticothalamic feedback regulations from S1 and S2, showing that S1 generally exerts inhibitory feedback regulation independent of external stimulation whereas S2 provides additional inhibition to the thalamic activity during nociceptive and tactile information processing in humans. These findings revealed that nociceptive and tactile information processing have similar hierarchical organization within the somatosensory system in the human brain.

Cervical spondylotic myelopathy (CSM) is a common disease of the elderly that is characterized by gait instability, sensorimotor deficits, etc. Recurrent symptoms including memory loss, poor attention, etc. have also been reported in recent studies. However, these have been rarely investigated in CSM patients. To investigate the cognitive deficits and their correlation with brain functional alterations, we conducted resting-state fMRI (rs-fMRI) signal variability. This is a novel indicator in the neuroimaging field for assessing the regional neural activity in CSM patients. Further, to explore the network changes in patients, functional connectivity (FC) and graph theory analyses were performed. Compared with the controls, the signal variabilities were significantly lower in the widespread brain regions especially at the default mode network (DMN), visual network, and somatosensory network. The altered inferior parietal lobule signal variability positively correlated with the cognitive function level. Moreover, the FC and the global efficiency of DMN increased in patients with CSM and positively correlated with the cognitive function level. According to the study results, (1) the cervical spondylotic myelopathy patients exhibited regional neural impairments, which correlated with the severity of cognitive deficits in the DMN brain regions, and (2) the increased FC and global efficiency of DMN can compensate for the regional impairment.