Brain Structural Connectivity Differences in Patients with Normal Cognition and Cognitive Impairment

Alterations of White Matter Connectivity in Adults with Essential Hypertension

Purpose

To explore the topology of the white matter network in individuals with essential hypertension by graph theory.

Patients and Methods

T1-weighted image and diffusion tensor imaging (DTI) data from 43 patients diagnosed with essential hypertension (EHT) and 33 individuals with normotension (healthy controls, HCs) were incorporated in this cross-sectional study. Furthermore, structural networks were constructed by graph theory to calculate whole brain network characteristics and intracerebral node characteristics.

Results

Both EHT and HC groups displayed small-worldness in their structural networks. The area under the curve (AUC) of the small-worldness coefficient (σ) was higher in the EHT group compared to the HC group, whereas the AUC of assortativity was lower in the EHT group in contrast to the HC group. The nodal clustering coefficient (CP) and local efficiency (Eloc) of the EHT group decreased in the right dorsolateral superior frontal gyrus and the left medial superior frontal gyrus. These values increased in the left anterior cingulate and paracingulate gyrus. Furthermore, weight and body mass index (BMI) were positively correlated with σ.

Conclusion

The EHT group showed brain network separation and integration dysfunction. Weight and BMI were positively correlated with σ. The data acquired in this investigation implied that altered structural connectivity in the prefrontal region may be a potential neuroimaging marker in EHT patients.

Disrupted structural connectivity and less efficient network system in patients with the treatment-naive adult attention-deficit/hyperactivity disorder

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder whose primary symptoms are hyperactivity, impulsivity, and inattention. Historically, ADHD was recognized as a disease of childhood and adolescence. However, many patients are known to have persistent symptoms into adulthood. Many researchers consider the neuropathology of ADHD to be based on abnormalities in multiple parallel and intersecting pathways rather than a single anatomical area, but such alterations remain to be clarified.

Methods

Using diffusion tensor imaging, we investigated differences in the global network metrics estimated by graph theory and the degree of connectivity between adjacent voxels within a white matter (WM) fascicle defined by the density of the diffusing spins (connectometry) between 19 drug-naive Japanese patients with adult ADHD and 19 matched healthy controls (HCs). In adult patients with ADHD, we examined the relationships between the symptomatology of ADHD and global network metrics and WM abnormalities.

Results

Compared with HCs, adult patients with ADHD showed a reduced rich-club coefficient and decreased connectivity in widely distributed WMs such as the corpus callosum, the forceps, and the cingulum bundle. Correlational analyses demonstrated that the general severity of ADHD symptoms was associated with several global network metrics, such as lower global efficiency, clustering coefficient, small worldness, and longer characteristic path length. The connectometry revealed that the severity of hyperactive/impulsive symptoms was associated with overconnectivity in the corticostriatal, corticospinal, and corticopontine tracts, the inferior fronto-occipital fasciculus, and the extreme capsule but dysconnectivity in the cerebellum. The severity of inattentive symptoms was associated with dysconnectivity in the intracerebellar circuit and some other fibers.

Conclusion

The results of the present study indicated that patients with treatment-naive adult ADHD showed disrupted structural connectivity, which contributes to less efficient information transfer in the ADHD brain and pathophysiology of ADHD.

Trial registration

UMIN Clinical Trials Registry (UMIN-CTR) UMIN000025183, Registered: 5 January 2017.

Diffusion spectrum imaging of patients with middle cerebral artery stenosis

OBJECTIVE

We aimed to detect microstructural changes in the brains of patients with unilateral middle cerebral artery (MCA) stenosis and to assess the integrity of the fiber structure and the small-world networks using diffusion spectrum imaging (DSI).

METHODS

A total of 21 healthy controls and 48 patients with unilateral MCA stenosis underwent 3.0 T MRI examination using DSI technique. Differential tractography, diffusion connectometry, and structural networks were performed by using DSI software. The correlation between the stenosis and quantitative anisotropy (QA) were analyzed using multiple regression models in the correlation tractography.

RESULTS

Differential tractography analysis showed that the left or right MCA stenosis group had decreased fiber connectivity in the brain network compared with the control group. The correlation tractography analysis of the patients with MCA stenosis showed that QA was negatively correlated with stenosis in the bilateral arcuate fasciculus, bilateral corticostriatal and corticothalamic pathway, bilateral corticopontine and corticospinal tract, right superior longitudinal fasciculus, right cingulum, corpus callosum, and left frontal aslant tract. Statistically significant differences were shown between the MCA stenosis groups and control group in graph density, global efficiency, network path length, and rich club coefficient.

CONCLUSION

DSI revealed that stroke-free patients with unilateral MCA stenosis have a disrupted structural network and damaged white matter fibers. Furthermore, the fiber connection disruption is more severe in the ipsilateral hemisphere and less prominent in the contralateral hemisphere in patients with unilateral MCA stenosis. Therefore, microstructural impairment has happened to patients with unilateral MCA stenosis even at a subclinical stage.

A Comparative Study of Diffusion Fiber Reconstruction Models for Pyramidal Tract Branches

Currently, comparative studies evaluating the quantification accuracy of pyramidal tracts (PT) and PT branches that were tracked based on four mainstream diffusion models are deficient. The present study aims to evaluate four mainstream models using the high-quality Human Connectome Project (HCP) dataset. Diffusion tensor imaging (DTI), diffusion spectral imaging (DSI), generalized Q-space sampling imaging (GQI), and Q-ball imaging (QBI) were used to construct the PT and PT branches in 50 healthy volunteers from the HCP. False and true PT fibers were identified based on anatomic information. One-way repeated measure analysis of variance and post hoc paired-sample t-test were performed to identify the best PT and PT branch quantification model. The number, percentage, and density of true fibers of PT obtained based on GQI and QBI were significantly larger than those based on DTI and DSI (all p < 0.0005, Bonferroni corrected), whereas false fibers yielded the opposite results (all p < 0.0005, Bonferroni corrected). More trunk branches (PT_trunk) were present in the four diffusion models compared with the upper limb (PT_Ulimb), lower limb (PT_Llimb), and cranial (PT_cranial) branches. In addition, significantly more true fibers were obtained in PT_trunk, PT_Ulimb, and PT_Llimb based on the GQI and QBI compared with DTI and DSI (all p < 0.0005, Bonferroni corrected). Finally, GQI-based group probabilistic maps showed that the four PT branches exhibited relatively unique spatial distributions. Therefore, the GQI and QBI represent better diffusion models for the PT and PT branches. The group probabilistic maps of PT branches have been shared with the public to facilitate more precise studies on the plasticity of and the damage to the motor pathway.