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

Advances in magnetic resonance imaging, particularly diffusion imaging, have allowed researchers to analyze brain connectivity. Identification of structural connectivity differences between patients with normal cognition, cognitive impairment, and dementia could lead to new biomarker discoveries that could improve dementia diagnostics. In our study, we analyzed 22 patients (11 control group patients, 11 dementia group patients) that underwent 3T MRI diffusion tensor imaging (DTI) scans and the Montreal Cognitive Assessment (MoCA) test. We reconstructed DTI images and used the Desikan–Killiany–Tourville cortical parcellation atlas. The connectivity matrix was calculated, and graph theoretical analysis was conducted using DSI Studio. We found statistically significant differences between groups in the graph density, network characteristic path length, small-worldness, global efficiency, and rich club organization. We did not find statistically significant differences between groups in the average clustering coefficient and the assortativity coefficient. These statistically significant graph theory measures could potentially be used as quantitative biomarkers in cognitive impairment and dementia diagnostics.

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Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia

BMC Neurology ◽

10.1186/s12883-021-02358-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dong Ah Lee ◽

Ho-Joon Lee ◽

Hyung Chan Kim ◽

Kang Min Park

Keyword(s):

Diffusion Tensor ◽

Focal Cortical Dysplasia ◽

Brain Mri ◽

Structural Connectivity ◽

Cortical Dysplasia ◽

Clustering Coefficient ◽

Connectivity Matrix ◽

Healthy Controls ◽

Local Efficiency ◽

Structural Volume

Abstract Background The aim of this study was to investigate alterations in structural connectivity and structural co-variance network in patients with focal cortical dysplasia (FCD). Methods We enrolled 37 patients with FCD and 35 healthy controls. All subjects underwent brain MRI with the same scanner and with the same protocol, which included diffusion tensor imaging (DTI) and T1-weighted imaging. We analyzed the structural connectivity based on DTI, and structural co-variance network based on the structural volume with T1-weighted imaging. We created a connectivity matrix and obtained network measures from the matrix using the graph theory. We tested the difference in network measure between patients with FCD and healthy controls. Results In the structural connectivity analysis, we found that the local efficiency in patients with FCD was significantly lower than in healthy controls (2.390 vs. 2.578, p = 0.031). Structural co-variance network analysis revealed that the mean clustering coefficient, global efficiency, local efficiency, and transitivity were significantly decreased in patients with FCD compared to those in healthy controls (0.527 vs. 0.635, p = 0.036; 0.545 vs. 0.648, p = 0.026; 2.699 vs. 3.801, p = 0.019; 0.791 vs. 0.954, p = 0.026, respectively). Conclusions We demonstrate that there are significant alterations in structural connectivity, based on DTI, and structural co-variance network, based on the structural volume, in patients with FCD compared to healthy controls. These findings suggest that focal lesions with FCD could affect the whole-brain network and that FCD is a network disease.

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Changes in Structural Connectivity Following a Cognitive Intervention in Children With Traumatic Brain Injury

Neurorehabilitation and Neural Repair ◽

10.1177/1545968316675430 ◽

2016 ◽

Vol 31 (2) ◽

pp. 190-201 ◽

Cited By ~ 19

Author(s):

Weihong Yuan ◽

Amery Treble-Barna ◽

McKay M. Sohlberg ◽

Beth Harn ◽

Shari L. Wade

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Mixed Model ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Cognitive Intervention ◽

Preliminary Evidence ◽

Brain Network ◽

Clustering Coefficient ◽

Characteristic Path Length

Objective. Structural connectivity analysis based on graph theory and diffusion tensor imaging tractography is a novel method that quantifies the topological characteristics in the brain network. This study aimed to examine structural connectivity changes following the Attention Intervention and Management (AIM) program designed to improve attention and executive function (EF) in children with traumatic brain injury (TBI). Methods. Seventeen children with complicated mild to severe TBI (13.66 ± 2.68 years; >12 months postinjury) completed magnetic resonance imaging (MRI) and neurobehavioral measures at time 1, 10 of whom completed AIM and assessment at time 2. Eleven matched healthy comparison (HC) children (13.37 ± 2.08 years) completed MRI and neurobehavioral assessment at both time points, but did not complete AIM. Network characteristics were analyzed to quantify the structural connectivity before and after the intervention. Results. Mixed model analyses showed that small-worldness was significantly higher in the TBI group than the HC group at time 1, and both small-worldness and normalized clustering coefficient decreased significantly at time 2 in the TBI group whereas the HC group remained relatively unchanged. Reductions in mean local efficiency were significantly correlated with improvements in verbal inhibition and both parent- and child-reported EF. Increased normalized characteristic path length was significantly correlated with improved sustained attention. Conclusion. The results provide preliminary evidence suggesting that graph theoretical analysis may be a sensitive tool in pediatric TBI for detecting ( a) abnormalities of structural connectivity in brain network and ( b) structural neuroplasticity associated with neurobehavioral improvement following a short-term intervention for attention and EF.

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Insights into the Connectivity of the Human Brain Using DTI

Nepalese Journal of Radiology ◽

10.3126/njr.v1i1.6330 ◽

2012 ◽

Vol 1 (1) ◽

pp. 78-91 ◽

Cited By ~ 2

Author(s):

S Kollias

Keyword(s):

Human Brain ◽

Molecular Diffusion ◽

Diffusion Tensor ◽

Wide Spectrum ◽

Diffusion Imaging ◽

Structural Connectivity ◽

Nerve Tissue ◽

Mammalian Brain ◽

Fibre Direction

Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows in vivo and non-destructive visualization of myeloarchitectonics in the neural tissue and provides quantitative estimates of WM integrity by measuring molecular diffusion. It is based on the phenomenon of diffusion anisotropy in the nerve tissue, in that water molecules diffuse faster along the neural fibre direction and slower in the fibre-transverse direction. On the basis of their topographic location, trajectory, and areas that interconnect the various fibre systems of the mammalian brain are divided into commissural, projectional and association fibre systems. DTI has opened an entirely new window on the white matter anatomy with both clinical and scientific applications. Its utility is found in both the localization and the quantitative assessment of specific neuronal pathways. The potential of this technique to address connectivity in the human brain is not without a few methodological limitations. A wide spectrum of diffusion imaging paradigms and computational tractography algorithms has been explored in recent years, which established DTI as promising new avenue, for the non-invasive in vivo mapping of structural connectivity at the macroscale level. Further improvements in the spatial resolution of DTI may allow this technique to be applied in the near future for mapping connectivity also at the mesoscale level. DOI: http://dx.doi.org/10.3126/njr.v1i1.6330 Nepalese Journal of Radiology Vol.1(1): 78-91

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Cognitive deficits and white matter abnormalities in never-treated first-episode schizophrenia

Translational Psychiatry ◽

10.1038/s41398-020-01049-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Mi Yang ◽

Shan Gao ◽

Xiangyang Zhang

Keyword(s):

Cognitive Impairment ◽

White Matter ◽

Diffusion Tensor ◽

Internal Capsule ◽

First Episode ◽

Control Group ◽

Pathophysiological Mechanism ◽

Healthy Controls ◽

Healthy Control ◽

First Episode Schizophrenia

Abstract Cognitive impairment is viewed as a core symptom of schizophrenia (SCZ), but its pathophysiological mechanism remains unclear. White matter (WM) disruption is considered to be a central abnormality that may contribute to cognitive impairment in SCZ patients. However, few studies have addressed the association between cognition and WM integrity in never-treated first-episode (NTFE) patients with SCZ. In this study, we used the MATRICS Consensus Cognitive Battery (MCCB) to evaluate cognitive function in NTFE patients (n = 39) and healthy controls (n = 30), and associated it with whole-brain fractional anisotropy (FA) values obtained via voxel-based diffusion tensor imaging. We found that FA was lower in five brain areas of SCZ patients, including the cingulate gyrus, internal capsule, corpus callosum, cerebellum, and brainstem. Compared with the healthy control group, the MCCB’s total score and 8 out of 10 subscores were significantly lower in NTFE patients (all p < 0.001). Moreover, in patients but not healthy controls, the performance in the Trail Making Test was negatively correlated with the FA value in the left cingulate. Our findings provide evidence that WM disconnection is involved in some cognitive impairment in the early course of SCZ.

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Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

Frontiers in Neurology ◽

10.3389/fneur.2021.671894 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jian Zhang ◽

Rosa Cortese ◽

Nicola De Stefano ◽

Antonio Giorgio

Keyword(s):

Multiple Sclerosis ◽

Cognitive Impairment ◽

Functional Connectivity ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Diffusion Kurtosis Imaging ◽

High Angular Resolution ◽

Diffusion Spectrum Imaging ◽

Analysis Methods ◽

Scan Time

Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

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A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.774607 ◽

2021 ◽

Vol 13 ◽

Author(s):

Cuibai Wei ◽

Shuting Gong ◽

Qi Zou ◽

Wei Zhang ◽

Xuechun Kang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Path Length ◽

Brain Networks ◽

Brain Regions ◽

Clustering Coefficient ◽

Characteristic Path Length ◽

Glucose Intake ◽

Item Scores ◽

Structural Brain

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

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Effects of Acupuncture on Cancer-Related Cognitive Impairment in Chinese Gynecological Cancer Patients: A Pilot Cohort Study

Integrative Cancer Therapies ◽

10.1177/1534735418777109 ◽

2018 ◽

Vol 17 (3) ◽

pp. 737-746 ◽

Cited By ~ 3

Author(s):

Yingchun Zeng ◽

Andy S. K. Cheng ◽

Ting Song ◽

Xiujie Sheng ◽

Shaojing Wang ◽

...

Keyword(s):

Cohort Study ◽

Cognitive Impairment ◽

White Matter ◽

Pilot Study ◽

Cancer Patients ◽

Gynecological Cancer ◽

Structural Connectivity ◽

Control Group ◽

Healthy Controls ◽

Neurocognitive Tests

Background: Among women in China, gynecological cancers are the second most common cancers after breast cancer. Cancer-related cognitive impairment (CRCI) has emerged as a significant problem affecting gynecological cancer survivors. While acupuncture has been used in different aspects of cancer care, the possible positive effects of acupuncture on cognitive impairment have received little attention. This study hypothesized that patients would demonstrate lower neurocognitive performance and lower structural connectivity compared to healthy controls. This pilot study also hypothesized that acupuncture may potentially be effective in treating CRCI of cancer patients by increasing brain structural connectivity and integrity. Methods: This prospective cohort study consisted of 3 stages: the first stage included a group of gynecological cancer patients and a group of age-matched healthy controls. This baseline stage used a core set of neurocognitive tests to screen patients with cognitive impairment and used a multimodal approach of brain magnetic resonance imaging (MRI) to explore the possible neurobiological mechanism of cognitive impairment in cancer patients, comparing the results with a group of noncancer controls. The second stage involved assigning CRCI patients into the acupuncture intervention group, while patients without CRCI were assigned into the cancer control group. The third stage was a postintervention assessment of neurocognitive function by the same set of neurocognitive tests at baseline. To explore the possible neurobiological basis of acupuncture for treating CRCI, this study also used a multimodal MRI approach to assess changes in brain structural connectivity, and neurochemical properties in patients at pre- and postacupuncture intervention. Results: This study found that the prevalence of cognitive impairment in Chinese gynecological cancer patients at diagnosis was 26.67%. When investigating the microstructural white matter in the brain, diffusion tensor imaging data in this study indicated that premorbid cognitive functioning (before clinical manifestations become evident) has already existed, as the global and local connectome properties in the entire patient group were lower than in the healthy control group. Using magnetic resonance spectroscopy, this study indicated there was a significant reduction of relative concentration of NAA ( N-acetyl aspartate) in the left hippocampus, comparing these results with healthy controls. Regarding the effects of acupuncture on reducing CRCI, patients in the acupuncture group reported better neurocognitive test performance after matching for age, menopausal status, cancer stage, and chemotherapy regimen dosage. On a microstructural level, acupuncture’s ability to reduce CRCI may be attributed to a reduction in demyelination and an enhancement of the neuronal viability of white matter in the hippocampus. Conclusion: This pilot study indicates that acupuncture is a promising intervention in treating CRCI in gynecological cancer patients undergoing chemotherapy; however, it requires evaluation in larger randomized controlled studies to definitively assess its benefit. By using a multimodal imaging approach, this pilot study also provides novel insights into the neurobiological basis of cognitive impairment on the human brain that has been induced by cancer and/or its treatment.

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Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer’s Disease

Brain Sciences ◽

10.3390/brainsci10110879 ◽

2020 ◽

Vol 10 (11) ◽

pp. 879

Author(s):

Angela Lombardi ◽

Nicola Amoroso ◽

Domenico Diacono ◽

Alfonso Monaco ◽

Giancarlo Logroscino ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Diffusion Tensor ◽

Verbal Learning ◽

Structural Connectivity ◽

Machine Learning Algorithms ◽

Disease Assessment ◽

Cognitive Score

Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer’s disease (AD). Several scores such as Alzheimer’s Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7).

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Sex Differences of Uncinate Fasciculus Structural Connectivity in Individuals with Conduct Disorder

BioMed Research International ◽

10.1155/2014/673165 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Jibiao Zhang ◽

Junling Gao ◽

Huqing Shi ◽

Bingsheng Huang ◽

Xiang Wang ◽

...

Keyword(s):

Conduct Disorder ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Gender Effects ◽

Control Group ◽

Healthy Controls ◽

Uncinate Fasciculus ◽

Female Patients ◽

Healthy Control ◽

Microstructural Integrity

Conduct disorder (CD) is one of the most common behavior disorders in adolescents, such as impulsivity, aggression, and running from school. Males are more likely to develop CD than females, and two previous diffusion tensor imaging (DTI) studies have demonstrated abnormal microstructural integrity in the uncinate fasciculus (UF) in boys with CD compared to a healthy control group. However, little is known about changes in the UF in females with CD. In this study, the UF was illustrated by tractography; then, the fractional anisotropy (FA), axial diffusivity, mean diffusion, radial diffusivity (RD), and the length and number of the UF fiber bundles were compared between male and female patients with CD and between female patients with CD and female healthy controls, as well as between males with CD and healthy males. We found that males with CD showed significantly higher FA of the bilateral UF and significantly lower RD of the left UF when comparing with females with CD. Meanwhile, significantly higher FA and lower RD of the bilateral UF were also found in boys with CD relative to the male healthy controls. Our results replicated previous reports that the microstructural integrity of the UF was abnormal in boys with CD. Additionally, our results demonstrated significant gender effects on the UF of patients with CD, which may indicate why boys have higher rates of conduct problems than girls.

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Increased hippocampal-inferior temporal cortex white matter connectivity following donepezil treatment in patients with mild cognitive impairment: A diffusion tensor probabilistic tractography study

10.21203/rs.3.rs-954650/v1 ◽

2021 ◽

Author(s):

Gwang-Won Kim ◽

Kwangsung Park ◽

Gwang-Woo Jeong

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

White Matter ◽

Diffusion Tensor ◽

Early Stage ◽

Structural Connectivity ◽

Temporal Cortex ◽

Mean Diffusivity ◽

Inferior Temporal Cortex ◽

Probabilistic Tractography

Abstract The incidence of Alzheimer’s disease (AD) has been increasing each year; however, few methods are available to identify the effects of treatment for AD. Defective hippocampus has been associated with mild cognitive impairment (MCI), an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. The purpose of this study was to evaluate the hippocampal white matter (WM) connectivity following donepezil treatment in patients with MCI using probabilistic tractography, and to further determine the WM integrity and changes in brain volume. Magnetic resonance imaging and diffusion tensor imaging (DTI) data of patients with MCI before and after 6-month donepezil treatment were acquired. Volumes and DTI scalars of 11 regions of interest comprising the frontal and temporal cortices and subcortical regions were measured. Seed-based structural connectivity analyses were focused on the hippocampus. Compared with healthy controls, patients with MCI showed significantly decreased hippocampal volume and WM connectivity with the superior frontal gyrus, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the amygdala (p < 0.05, Bonferroni-corrected). After six months of donepezil treatment, patients with MCI showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected), which was normalized to the healthy control. These findings will be useful in developing theories to describe the etiology of MCI and the therapeutic role of anticholinesterases.

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