Individual variability in functional connectivity of human auditory cortex.

Individual variability in functional connectivity underlies individual differences in cognition and behaviors, yet its association with functional specialization in the auditory cortex remains elusive. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, this study was designed to investigate the spatial distribution of auditory cortex individual variability in its whole-brain functional network architecture. An inherent hierarchical axis of the variability was discerned, which radiates from the medial to lateral orientation, with the left auditory cortex demonstrating more pronounced variations than the right. This variability exhibited a significant correlation with the variations in structural and functional metrics in the auditory cortex. Four auditory cortex subregions, which were identified from a clustering analysis based on this variability, exhibited unique connectional fingerprints and cognitive maps, with certain subregions showing specificity to speech perception functional activation. Moreover, the lateralization of the connectional fingerprint exhibited a U-shaped trajectory across the subregions. These findings emphasize the role of individual variability in functional connectivity in understanding cortical functional organization, as well as in revealing its association with functional specialization from the activation, connectome, and cognition perspectives.

Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging.

Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.

Disrupted Topologic Efficiency of White Matter Structural Connectome in Individuals with Subjective Cognitive Decline.

Purpose To determine whether individuals with subjective cognitive decline (SCD), which is defined by memory complaints with normal performance at objective neuropsychologic examinations, exhibit disruptions of white matter (WM) connectivity and topologic alterations of the brain structural connectome. Materials and Methods Diffusion-tensor magnetic resonance imaging and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 36 participants with SCD (21 women: mean age, 62.0 years ± 8.6 [standard deviation]; age range, 42-76 years; 15 men: mean age, 65.5 years ± 8.9; age range, 51-80 years) and 51 age-, sex-, and years of education-matched healthy control participants (33 women: mean age, 63.7 years ± 8.8; age range, 46-83 years; 18 men: mean age, 59.4 years ± 9.3; age range, 43-75 years). Individual WM networks were constructed for each participant, and the network properties between two groups were compared with a linear regression model. Results Graph theory analyses revealed that the participants with SCD had less global efficiency (P = .001) and local efficiency (P = .008) compared with the healthy control participants. Lower regional efficiency was mainly distributed in the bilateral prefrontal regions and left thalamus (P < .05, corrected). Furthermore, a disrupted subnetwork was observed that consisted of widespread anatomic connections (P < .05, corrected), which has the potential to discriminate individuals with SCD from control participants. Moreover, similar hub distributions and less connection strength between the hub regions (P = .023) were found in SCD. Importantly, diminished strength of the rich-club and local connections was correlated with the impaired memory performance in patients with SCD (rich-club connection: r = 0.43, P = .011; local connection: r = 0.36, P = .037). Conclusion This study demonstrated disrupted topologic efficiency of the brain's structural connectome in participants with SCD and provided potential connectome-based biomarkers for the early detection of cognitive impairment in elderly individuals. © RSNA, 2017 Online supplemental material is available for this article.

Alterations in brain structure and function associated with pediatric growth hormone deficiency: A multi-modal magnetic resonance imaging study.

Introduction: Pediatric growth hormone deficiency (GHD) is a disease resulting from impaired growth hormone/insulin-like growth factor-1 (IGF-1) axis but the effects of GHD on children's cognitive function, brain structure and brain function were not yet fully illustrated. Methods: Full Wechsler Intelligence Scales for Children, structural imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were assessed in 11 children with GHD and 10 matched healthy controls. Results: (1) The GHD group showed moderate cognitive impairment, and a positive correlation existed between IGF-1 levels and cognitive indices. (2) Mean diffusivity was significantly increased in both corticospinal tracts in GHD group. (3) There were significant positive correlations between IGF-1 levels and volume metrics of left thalamus, left pallidum and right putamen but a negative correlation between IGF-1 levels and cortical thickness of the occipital lobe. And IGF-1 levels negatively correlated with fractional anisotropy in the superior longitudinal fasciculus and right corticospinal tract. (4) Regional homogeneity (ReHo) in the left hippocampus/parahippocampal gyrus was negatively correlated with IGF-1 levels; the amplitude of low-frequency fluctuation (ALFF) and ReHo in the paracentral lobe, postcentral gyrus and precentral gyrus were also negatively correlated with IGF-1 levels, in which region ALFF fully mediates the effect of IGF-1 on working memory index. Conclusion: Multiple subcortical, cortical structures, and regional neural activities might be influenced by serum IGF-1 levels. Thereinto, ALFF in the paracentral lobe, postcentral gyrus and precentral gyrus fully mediates the effect of IGF-1 on the working memory index.

Difference in Amyloid Load Between Single Memory Domain and Multidomain Subjective Cognitive Decline: A Study from the SILCODE.

BACKGROUND: Subjective cognitive decline (SCD), an at-risk condition of Alzheimer's disease (AD), can involve various cognitive domains, such as memory, language, planning, and attention. OBJECTIVE: We aim to explore the difference in amyloid load between the single memory domain SCD (sd-SCD) and the multidomain SCD (md-SCD) and assess the relationship of amyloid pathology with quantitative SCD scores and objective cognition. METHODS: A total of 63 SCD participants from the SILCODE study underwent the clinical evaluation, neuropsychological assessment, and 18F-florbetapir PET scan. Global amyloid standard uptake value ratio (SUVr) was calculated. Additionally, regional amyloid SUVr was quantified in 12 brain regions of interests. A nonparametric rank ANCOVA was used to compare the global and regional amyloid SUVr between the md-SCD (n = 34) and sd-SCD (n = 29) groups. A multiple linear regression analysis was conducted to test the relationship of amyloid SUVr with quantitative SCD scores and objective cognition. RESULTS: Compared with individuals with sd-SCD, individuals with md-SCD had increased global amyloid SUVr (F = 5.033, p = 0.029) and regional amyloid SUVr in the left middle temporal gyrus (F = 12.309, p = 0.001; Bonferroni corrected), after controlling for the effects of age, sex, and education. When pooling all SCD participants together, the increased global amyloid SUVr was related with higher SCD-plus sum scores and lower Auditory Verbal Learning Test-delayed recall scores. CONCLUSION: According to our findings, individuals with md-SCD showed higher amyloid accumulation than individuals with sd-SCD, suggesting that md-SCD may experience a more advanced stage of SCD. Additionally, increased global amyloid load was predictive of a poorer episodic memory function in SCD individuals.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome.

Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown. Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters. Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%. Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.

Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment.

Amnestic mild cognitive impairment (aMCI) is accompanied by the accelerated cognitive decline and rapid brain degeneration with aging. However, the age-related alterations of the topological organization of the brain connectome in aMCI patients remained largely unknown. In this study, we constructed the brain structural connectome in 51 aMCI patients and 51 healthy controls by diffusion magnetic resonance imaging and deterministic tractography. The different age-related alteration patterns of the global and regional network metrics between aMCI patients and healthy controls were assessed by a linear regression model. Compared with healthy controls, significantly decreased global and local network efficiency in aMCI patients were found. When correlating network efficiency with age, we observed a significant decline in network efficiency with aging in the aMCI patients, while not in the healthy controls. The age-related decreases of nodal efficiency in aMCI patients were mainly distributed in the key regions of the default-mode network, such as precuneus, anterior cingulate gyrus, and parahippocampal gyrus. In addition, age-related decreases in the connection strength of the edges between peripheral nodes were observed in aMCI patients. Moreover, the decreased regional efficiency of the parahippocampal gyrus was correlated with impaired memory performances in patients. The present study suggests an age-related disruption of the topological organization of the brain structural connectome in aMCI patients, which may provide evidence for different neural mechanisms underlying aging in aMCI and may serve as a potential imaging marker for the early diagnosis of Alzheimer's disease.